double blind randomized experimental study

Double-Blind Experimental Study And Procedure Explained

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What is a Blinded Study?

• Binding, or masking, refers to withholding information regarding treatment allocation from one or more participants in a clinical research study, typically in randomized control trials .
• A blinded study prevents the participants from knowing about their treatment to avoid bias in the research. Any information that can influence the subjects is withheld until the completion of the research.
• Blinding can be imposed on any participant in an experiment, including researchers, data collectors, evaluators, technicians, and data analysts. 
• Good blinding can eliminate experimental biases arising from the subjects’ expectations, observer bias, confirmation bias, researcher bias, observer’s effect on the participants, and other biases that may occur in a research test.
• Studies may use single-, double- or triple-blinding. A trial that is not blinded is called an open trial.

Double-Blind Studies

Double-blind studies are those in which neither the participants nor the experimenters know who is receiving a particular treatment.

Double blinding prevents bias in research results, specifically due to demand characteristics or the placebo effect.

Demand characteristics are subtle cues from researchers that can inform the participants of what the experimenter expects to find or how participants are expected to behave.

If participants know which group they are assigned to, they might change their behavior in a way that would influence the results. Similarly, if a researcher knows which group a participant is assigned to, they might act in a way that reveals the assignment or influences the results.

Double-blinding attempts to prevent these risks, ensuring that any difference(s) between the groups can be attributed to the treatment. 

On the other hand, single-blind studies are those in which the experimenters are aware of which participants are receiving the treatment while the participants are unaware.

Single-blind studies are beneficial because they reduce the risk of errors due to subject expectations. However, single-blind studies do not prevent observer bias, confirmation bias , or bias due to demand characteristics.

Because the experiments are aware of which participants are receiving which treatments, they are more likely to reveal subtle clues that can accidentally influence the research outcome.

Double-blind studies are considered the gold standard in research because they help to control for experimental biases arising from the subjects’ expectations and experimenter biases that emerge when the researchers unknowingly influence how the subjects respond or how the data is collected.

Using the double-blind method improves the credibility and validity of a study .

Example Double-Blind Studies

Rostock and Huber (2014) used a randomized, placebo-controlled, double-blind study to investigate the immunological effects of mistletoe extract. However, their study showed that double-blinding is impossible when the investigated therapy has obvious side effects. 

Using a double-blind study, Kobak et al. (2005) found that S t John’s wort ( Hypericum perforatum ) is not an efficacious treatment for anxiety disorder, specifically OCD.

Using the Yale–Brown Obsessive–Compulsive Scale (Y-BOCS), they found that the mean change with St John’s wort was not significantly different from the mean change found with placebo. 

Cakir et al. (2014) conducted a randomized, controlled, and double-blind study to test the efficacy of therapeutic ultrasound for managing knee osteoarthritis.

They found that all assessment parameters significantly improved in all groups without a significant difference, suggesting that therapeutic ultrasound provided no additional benefit in improving pain and functions in addition to exercise training.

Using a randomized double-blind study, Papachristofilou et al. (2021) found that whole-lung LDRT failed to improve clinical outcomes in critically ill patients admitted to the intensive care unit requiring mechanical ventilation for COVID-19 pneumonia.

Double-Blinding Procedure

Double blinding is typically used in clinical research studies or clinical trials to test the safety and efficacy of various biomedical and behavioral interventions.

In such studies, researchers tend to use a placebo. A placebo is an inactive substance, typically a sugar pill, that is designed to look like the drug or treatment being tested but has no effect on the individual taking it. 

The placebo pill was given to the participants who were randomly assigned to the control group. This group serves as a baseline to determine if exposure to the treatment had any significant effects.

Those randomly assigned to the experimental group are given the actual treatment in question. Data is collected from both groups and then compared to determine if the treatment had any impact on the dependent variable.

All participants in the study will take a pill or receive a treatment, but only some of them will receive the real treatment under investigation while the rest of the subjects will receive a placebo. 

With double blinding, neither the participants nor the experimenters will have any idea who receives the real drug and who receives the placebo. 

For Example

A common example of double-blinding is clinical studies that are conducted to test new drugs.

In these studies, researchers will use random assignment to allocate patients into one of three groups: the treatment/experimental group (which receives the drug), the placebo group (which receives an inactive substance that looks identical to the treatment but has no drug in it), and the control group (which receives no treatment).

Both participants and researchers are kept unaware of which participants are allocated to which of the three groups.

The effects of the drug are measured by recording any symptoms noticed in the patients.

Once the study is unblinded, and the researchers and participants are made aware of who is in which group, the data can be analyzed to determine whether the drug had effects that were not seen in the placebo or control group, but only in the experimental group. 

Double-blind studies can also be beneficial in nonmedical interventions, such as psychotherapIes.

Reduces risk of bias

Double-blinding can eliminate, or significantly reduce, both observer bias and participant biases.

Because both the researcher and the subjects are unaware of the treatment assignments, it is difficult for their expectations or behaviors to influence the study.

Results can be duplicated

The results of a double-blind study can be duplicated, enabling other researchers to follow the same processes, apply the same test item, and compare their results with the control group.

If the results are similar, then it adds more validity to the ability of a medication or treatment to provide benefits. 

It tests for three groups

Double-blind studies usually involve three groups of subjects: the treatment group, the placebo group, and the control group.

The treatment and placebo groups are both given the test item, although the researcher does not know which group is getting real treatment or placebo treatment.

The control group doesn’t receive anything because it serves as the baseline against which the other two groups are compared.

This is an advantage because if subjects in the placebo group improved more than the subjects in the control group, then researchers can conclude that the treatment administered worked.

Applicable across multiple industries

Double-blind studies can be used across multiple industries, such as agriculture, biology, chemistry, engineering, and social sciences.

Double-blind studies are used primarily by the pharmaceutical industry because researchers can look directly at the impact of medications. 

Disadvantages

Inability to blind.

In some types of research, specifically therapeutic, the treatment cannot always be disguised from the participant or the experimenter. In these cases, you must rely on other methods to reduce bias.

Additionally, imposing blinding may be impossible or unethical for some studies. 

Double-blinding can be expensive because the researcher has to examine all the possible variables and may have to use different groups to gather enough data. 

Small Sample Size

Most double-blind studies are too small to provide a representative sample. To be effective, it is generally recommended that double-blind trials include around 100-300 participants.

Studies involving fewer than 30 participants generally can’t provide proof of a theory. 

Negative Reaction to Placebo

In some instances, participants can have adverse reactions to the placebo, even producing unwanted side effects as if they were taking a real medication. 

It doesn’t reflect real-life circumstances

When participants receive treatment or medication in a double-blind placebo study, each individual is told that the item in question might be real medication or a placebo.

This artificial situation does not represent real-life circumstances because when a patient receives a pill after going to the doctor in the real-world, they are told that the product is actual medicine intended to benefit them.

When situations don’t feel realistic to a participant, then the quality of the data can decrease exponentially.

What is the difference between a single-blind, double-blind, and triple-blind study?

In a single-blind study, the experimenters are aware of which participants are receiving the treatment while the participants are unaware.

In a double-blind study, neither the patients nor the researchers know which study group the patients are in. In a triple-blind study, neither the patients, clinicians, nor the people carrying out the statistical analysis know which treatment the subjects had.

Is a double-blind study the same as a randomized clinical trial?

Yes, a double-blind study is a form of a randomized clinical trial in which neither the participants nor the researcher know if a subject is receiving the experimental treatment, a standard treatment, or a placebo.

Are double-blind studies ethical?

Double blinding is ethical only if it serves a scientific purpose. In most circumstances, it is unethical to conduct a double-blind placebo controlled trial where standard therapy exists.

What is the purpose of randomization using double blinding?

Randomization with blinding avoids reporting bias, since no one knows who is being treated and who is not, and thus all treatment groups should be treated the same. This reduces the influence of confounding variables and improves the reliability of clinical trial results.

Why are double-blind experiments considered the gold standard?

Randomized double-blind placebo control studies are considered the “gold standard” of epidemiologic studies as they provide the strongest possible evidence of causality.

Additionally, because neither the participants nor the researchers know who has received what treatment, double-blind studies minimize the placebo effect and significantly reduce bias.

Can blinding be used in qualitative studies?

Yes, blinding is used in qualitative studies .

Cakir, S., Hepguler, S., Ozturk, C., Korkmaz, M., Isleten, B., & Atamaz, F. C. (2014). Efficacy of therapeutic ultrasound for the management of knee osteoarthritis: a randomized, controlled, and double-blind study. American journal of physical medicine & rehabilitation , 93 (5), 405-412.

Kobak, K. A., Taylor, L. V., Bystritsky, A., Kohlenberg, C. J., Greist, J. H., Tucker, P., … & Vapnik, T. (2005). St John’s wort versus placebo in obsessive–compulsive disorder: results from a double-blind study. International Clinical Psychopharmacology , 20 (6), 299-304.

Papachristofilou, A., Finazzi, T., Blum, A., Zehnder, T., Zellweger, N., Lustenberger, J., … & Siegemund, M. (2021). Low-dose radiation therapy for severe COVID-19 pneumonia: a randomized double-blind study. International Journal of Radiation Oncology* Biology* Physics , 110 (5), 1274-1282. Rostock, M., & Huber, R. (2004). Randomized and double-blind studies–demands and reality as demonstrated by two examples of mistletoe research. Complementary Medicine Research , 11 (Suppl. 1), 18-22.

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• What Is a Double-Blind Study? | Introduction & Examples

What Is a Double-Blind Study? | Introduction & Examples

Published on 6 May 2022 by Lauren Thomas . Revised on 17 October 2022.

In experimental research , subjects are randomly assigned to either a treatment or control group . A double-blind study withholds each subject’s group assignment from both the participant and the researcher performing the experiment.

If participants know which group they are assigned to, there is a risk that they might change their behaviour in a way that would influence the results. If researchers know which group a participant is assigned to, they might act in a way that reveals the assignment or directly influences the results.

Double blinding guards against these risks, ensuring that any difference between the groups can be attributed to the treatment.

Table of contents

Different types of blinding, importance of blinding, frequently asked questions about double-blind studies.

Blinding means withholding which group each participant has been assigned to. Studies may use single, double or triple blinding.

Single blinding occurs in many different kinds of studies, but double and triple blinding are mainly used in medical research.

Single blinding

If participants know whether they were assigned to the treatment or control group, they might modify their behaviour as a result, potentially changing their eventual outcome.

In a single-blind experiment, participants do not know which group they have been placed in until after the experiment has finished.

If participants in the control group realise they have received a fake vaccine and are not protected against the flu, they might modify their behaviour in ways that lower their chances of becoming sick – frequently washing their hands, avoiding crowded areas, etc. This behaviour could narrow the gap in sickness rates between the control group and the treatment group, thus making the vaccine seem less effective than it really is.

Double blinding

When the researchers administering the experimental treatment are aware of each participant’s group assignment, they may inadvertently treat those in the control group differently from those in the treatment group. This could reveal to participants their group assignment, or even directly influence the outcome itself.

In double-blind experiments, the group assignment is hidden from both the participant and the person administering the experiment.

If these experimenters knew which vaccines were real and which were fake, they might accidentally reveal this information to the participants, thus influencing their behaviour and indirectly the results.

They could even directly influence the results. For instance, if experimenters expect the vaccine to result in lower levels of flu symptoms, they might accidentally measure symptoms incorrectly, thus making the vaccine appear more effective than it really is.

Triple blinding

Although rarely implemented, triple-blind studies occur when group assignment is hidden not only from participants and administrators, but also from those tasked with analysing the data after the experiment has concluded.

Researchers may expect a certain outcome and analyse the data in different ways until they arrive at the outcome they expected, even if it is merely a result of chance.

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Blinding helps ensure a study’s internal validity , or the extent to which you can be confident any link you find in your study is a true cause-and-effect relationship.

Since non-blinded studies can result in participants modifying their behaviour or researchers finding effects that do not really exist, blinding is an important tool to avoid bias in all types of scientific research.

Risk of unblinding

Unblinding occurs when researchers have blinded participants or experimenters, but they become aware of who received which treatment before the experiment has ended.

This may result in the same outcomes as would have occurred without any blinding.

You randomly assign some students to the new programme (the treatment group), while others are instructed with a standard programme (the control group). You use single blinding: you do not inform students whether they are receiving the new instruction programme or the standard one.

If students become aware of which programme they have been assigned to – for example, by talking to previous students about the content of the programme – they may change their behaviour. Students in the control group might work harder on their reading skills to make up for not receiving the new programme, or conversely to put in less effort instead since they might believe the other students will do better than them anyway.

Inability to blind

Double or triple blinding is often not possible. While medical experiments can usually use a placebo or fake treatment for blinding, in other types of research, the treatment sometimes cannot be disguised from either the participant or the experimenter. For example, many treatments that physical therapists perform cannot be faked.

In such cases, you must rely on other methods to reduce bias.

• Running a single- rather than double- or triple-blind study. Sometimes, although you might not be able to hide what each subject receives, you can still prevent them from knowing whether they are in the treatment or control group. Single blinding is particularly useful in non-medical studies where you cannot use a placebo in the control group.
• Relying on objective measures that participants and experimenters have less control over rather than subjective ones, like measuring fever rather than self-reported pain. This should reduce the possibility that participants or experimenters could influence the results.
• Pre-registering data analysis techniques. This will prevent researchers from trying different measures of analysis until they arrive at the answer they’re expecting.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

Blinding is important to reduce bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analysing the data.

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Double-Blind Studies in Research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

A double-blind study is one in which neither the participants nor the experimenters know who is receiving a particular treatment. This procedure is utilized to prevent bias in research results. Double-blind studies are particularly useful for preventing bias due to demand characteristics or the placebo effect .

For example, let's imagine that researchers are investigating the effects of a new drug. In a double-blind study, the researchers who interact with the participants would not know who was receiving the actual drug and who was receiving a placebo.

A Closer Look at Double-Blind Studies

Let’s take a closer look at what we mean by a double-blind study and how this type of procedure works. As mentioned previously, double-blind indicates that the participants and the experimenters are unaware of who is receiving the real treatment. What exactly do we mean by ‘treatment'? In a psychology experiment, the treatment is the level of the independent variable that the experimenters are manipulating.

This can be contrasted with a single-blind study in which the experimenters are aware of which participants are receiving the treatment while the participants remain unaware.

In such studies, researchers may use what is known as a placebo. A placebo is an inert substance, such as a sugar pill, that has no effect on the individual taking it. The placebo pill is given to participants who are randomly assigned to the control group. A control group is a subset of participants who are not exposed to any levels of the independent variable . This group serves as a baseline to determine if exposure to the independent variable had any significant effects.

Those randomly assigned to the experimental group are given the treatment in question. Data collected from both groups are then compared to determine if the treatment had some impact on the dependent variable .

All participants in the study will take a pill, but only some of them will receive the real drug under investigation. The rest of the subjects will receive an inactive placebo. With a double-blind study, the participants and the experimenters have no idea who is receiving the real drug and who is receiving the sugar pill.

Double-blind experiments are simply not possible in some scenarios. For example, in an experiment looking at which type of psychotherapy is the most effective, it would be impossible to keep participants in the dark about whether or not they actually received therapy.

Reasons to Use a Double-Blind Study

So why would researchers opt for such a procedure? There are a couple of important reasons.

• First, since the participants do not know which group they are in, their beliefs about the treatment are less likely to influence the outcome.
• Second, since researchers are unaware of which subjects are receiving the real treatment, they are less likely to accidentally reveal subtle clues that might influence the outcome of the research.  

The double-blind procedure helps minimize the possible effects of experimenter bias.   Such biases often involve the researchers unknowingly influencing the results during the administration or data collection stages of the experiment. Researchers sometimes have subjective feelings and biases that might have an influence on how the subjects respond or how the data is collected.

In one research article, randomized double-blind placebo studies were identified as the "gold standard" when it comes to intervention-based studies.   One of the reasons for this is the fact that random assignment reduces the influence of confounding variables.

Imagine that researchers want to determine if consuming energy bars before a demanding athletic event leads to an improvement in performance. The researchers might begin by forming a pool of participants that are fairly equivalent regarding athletic ability. Some participants are randomly assigned to a control group while others are randomly assigned to the experimental group.

Participants are then be asked to eat an energy bar. All of the bars are packaged the same, but some are sports bars while others are simply bar-shaped brownies. The real energy bars contain high levels of protein and vitamins, while the placebo bars do not.

Because this is a double-blind study, neither the participants nor the experimenters know who is consuming the real energy bars and who is consuming the placebo bars.

The participants then complete a predetermined athletic task, and researchers collect data performance. Once all the data has been obtained, researchers can then compare the results of each group and determine if the independent variable had any impact on the dependent variable.  

A Word From Verywell

A double-blind study can be a useful research tool in psychology and other scientific areas. By keeping both the experimenters and the participants blind, bias is less likely to influence the results of the experiment. 

A double-blind experiment can be set up when the lead experimenter sets up the study but then has a colleague (such as a graduate student) collect the data from participants. The type of study that researchers decide to use, however, may depend upon a variety of factors, including characteristics of the situation, the participants, and the nature of the hypothesis under examination.

National Institutes of Health. FAQs About Clinical Studies .

Misra S. Randomized double blind placebo control studies, the "Gold Standard" in intervention based studies . Indian J Sex Transm Dis AIDS . 2012;33(2):131-4. doi:10.4103/2589-0557.102130

Goodwin, CJ. Research In Psychology: Methods and Design . New York: John Wiley & Sons; 2010.

Kalat, JW. Introduction to Psychology . Boston, MA: Cengage Learning; 2017.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Double Blind Study – Blinded Experiments

In science and medicine, a blind study or blind experiment is one in which information about the study is withheld from the participants until the experiment ends. The purpose of blinding an experiment is reducing bias, which is a type of error . Sometimes blinding is impractical or unethical, but in many experiments it improves the validity of results. Here is a look at the types of blinding and potentials problems that arise.

Single Blind, Double Blind, and Triple Blind Studies

The three types of blinding are single blinding, double blinding, and triple blinding:

Single Blind Study

In a single blind study , the researchers and analysis team know who gets a treatment, but the experimental subjects do not. In other words, the people performing the study know what the independent variable is and how it is being tested. The subjects are unaware whether they are receiving a placebo or a treatment. They may even be unaware what, exactly, is being studied.

Example: Violin Study

For example, consider an experiment that tests whether or not violinists can tell the difference a Stradivarius violin (generally regarded as superior) and a modern violin. The researchers know the type of violin they hand to a violinist, but the musician does not (is blind). In case you’re curious, in an actual experiment performed by Claudia Fritz and Joseph Curtin, it turned out violinists actually can’t tell the instruments apart.

Double Blind Study

In a double blind study, neither the researchers nor subjects know which group receives a treatment and which gets a placebo .

Example: Drug Trial

Many drug trials are double-blinded, where neither the doctor nor patient knows whether the drug or a placebo is administered. So, who gets the drug or the placebo is randomly assigned (without the doctor knowing who gets what). The inactive ingredients, color, and size of a pill (for example) are the same whether it is the treatment or placebo.

Triple Blind Study

A triple blind study includes an additional level of blinding. So, the data analysis team or the group overseeing an experiment is blind, in addition to the researchers and subjects.

Example: Vaccine Study

Triple blind studies are common as part of the vaccine approval process. Here, the people who analyze vaccine effectiveness collate data from many test sites and are unaware of which group a participant belongs to.

Some guidelines advocate for removing terms like “single blind” and “double blind” because they do not inherently describe which party is blinded. For example, a double blind study could mean the subjects and scientists are blind or it could mean the subjects and assessors are blind. When you describe blinding in an experiment, report who is blinded and what information is concealed.

The point of blinding is minimizing bias. Subjects have expectations if they know they receive a placebo versus a treatment. And, researchers have expectations regarding the expected outcome. For example, confirmation bias occurs when an investigator favors outcomes that support pre-existing research or the scientist’s own beliefs.

Unblinding is when masked information becomes available. In experiments with humans, intentional unblinding after a study concludes is typical. This way, a subject knows whether or not they received a treatment or placebo. Unblinding after a study concludes does not introduce bias because the data has already been collected and analyzed.

However, premature unblinding also occurs. For example, a doctor reviewing bloodwork often figures out who is getting a treatment and who is getting a placebo. Similarly, patients feeling an effect from a pill or injection suspect they are in the treatment group. One safeguard against this is an active placebo. An active placebo causes side effects, so it’s harder to tell treatment and placebo groups apart just based on how a patient feels.

Although premature unblinding affects the outcome of the results, it isn’t usually reported. This is a problem because unintentional unblinding favors false positives, at least in medicine. For example, if subjects believe they are receiving treatment, they often feel better even if a therapy isn’t effective. Premature unblinding is one of the issues at the heart of the debate about whether or not antidepressants are effective. But, it applies to all blind studies.

Uses of Blind Studies

Of course, blind studies are valuable in medicine and scientific research. But, they also have other applications.

For example, in a police lineup, having an officer familiar with the suspects can influence a witness’s selection. A better option is a blind procedure, using an office who does not know a suspect’s identity. Product developers routinely use blind studies for determining consumer preference. Orchestras use blind judging for auditions. Some employers and educational institutions use blind data for application selection.

• Bello, Segun; Moustgaard, Helene; Hróbjartsson, Asbjørn (October 2014). “The risk of unblinding was infrequently and incompletely reported in 300 randomized clinical trial publications”. Journal of Clinical Epidemiology . 67 (10): 1059–1069. doi: 10.1016/j.jclinepi.2014.05.007
• Daston, L. (2005). “Scientific Error and the Ethos of Belief”. Social Research . 72 (1): 18. doi: 10.1353/sor.2005.0016
• MacCoun, Robert; Perlmutter, Saul (2015). “Blind analysis: Hide results to seek the truth”. Nature . 526 (7572): 187–189. doi: 10.1038/526187a
• Moncrieff, Joanna; Wessely, Simon; Hardy, Rebecca (2018). “Meta-analysis of trials comparing antidepressants with active placebos”. British Journal of Psychiatry . 172 (3): 227–231. doi: 10.1192/bjp.172.3.227
• Schulz, Kenneth F.; Grimes, David A. (2002). “Blinding in randomised trials: hiding who got what”. Lancet . 359 (9307): 696–700. doi: 10.1016/S0140-6736(02)07816-9

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What is a Double-Blind Trial?

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When drugs or vaccines are being trialed for their effectiveness, there are typically several stages. Double-blind trials are seen as the most reliable type of study because they involve neither the participant nor the doctor knowing who has received what treatment. The aim of this is to minimize the placebo effect and minimize bias.

How they work

In double-blind trials, the treatment patients have is unknown to both patients and doctors until after the study is concluded. This differs from other types of trials, such as simple blind trials where only the patients are unaware of the treatment they are receiving, whereas the doctors know.

Double-blind trials are a form of randomized trials and can be ‘upgraded’ to triple-blind trials, in which the statisticians or data clean-up personnel are also blind to treatments.

To be effective, it is generally recommended that double-blind trials include around 100-300 people. If treatments are highly effective, smaller numbers can be used but if only 30 or so patients are enrolled the study is unlikely to be beneficial.

The assignment of patients into treatments is typically done by computers, where the computer assigns each patient a code number and treatment group. The doctor and patients only know the code number to avoid bias, hence allowing the study to be double-blind.

Double-blind trials can come in different varieties. Double-blind, placebo-controlled studies involve no one knowing the treatment assignments to remove the chance of placebo effects. In a double-blind comparative trial, a new treatment is often compared to the standard drug. This allows researchers to compare an established drug to a new one to establish which one is more advantageous.

However, unlike double-blind, placebo-controlled trials, they are not very good at statistically evaluating if a treatment is effective overall.

Benefits of double-blind trials

Double-blind trials remove any power of suggestion, as no one involved knows the treatment patients receive. This means that doctors carrying out the study do not know and cannot accidentally tip off participants. Similarly, the doctors not being aware of the treatments means they do not unconsciously bias their interpretation of the study results.

The main principle behind double-blind and randomized trials, as opposed to simple blind trials, is to avoid bias in the treatment or experimental set-up. For example, if researchers are aware of the different treatment groups are getting, they may avoid assigning more unwell patients to the treatment group. Therefore, any effect seen by the treatment may have been related to how unwell a patient was to start with, rather than the efficacy of the drug.

COVID-19 and double-blind trials

Double-blind trials are usually needed for drugs and treatments to get approval to be used in many countries. However, good, comprehensive double-blind trials take time and require many participants. This has been especially problematic during the COVID-19 pandemic, as the world has searched for pharmaceutical treatment options to improve survival and for vaccines to prevent the spread of this virus.

In terms of treatment, many drugs have been tested in double-blind trials. The antiviral nucleoside analog remdesivir has been tested in several double-blind trials and was the first drug to gain full FDA approval for use against COVID-19 in October 2020.

However, the results of trials have been conflicting, and some experts remained unconvinced of its benefits. In November 2020, the World Health Organization recommended against the use of the drug for COVID-19 and a global randomized trial came to the conclusion in February 2021 that remdesivir has little to no effect when used on hospitalized COVID-19 patients. The drug is still used in the US.

Multiple candidates for a COVID-19 vaccine have been identified and moved on to phase II and phase III trials, which often involve double-blind methods. These need to be conducted over meaningful timeframes to ensure any initial differences between the control and the treatment groups last in the long term.

Several different vaccines are now available (March 2021) due to mixed approval and emergency approval by governments and organizations. This has been an exceptional time for vaccine trials as the typical course of development has been sped up. What would usually take years has taken months.

Many countries have given limited or early approval to vaccines for emergency use before detailed phase III data has been publicized, based on preliminary evidence of effectivity and safety. This comes with some risks.

Another topic of discussion that has come about as a result of COVID-19 is the ethics of keeping patients blind during the trial as vaccine effectivity is supported. Whilst keeping the blind aspect is essential to achieving valuable and reliable information about long-term effects, there is an argument that blind participants who have received a placebo should be able to receive a vaccine as more become available.

• Cancer Research UK. 2019. Randomized Trials . [online] Available at: <https://www.cancerresearchuk.org/find-a-clinical-trial/what-clinical-trials-are/randomised-trials> [Accessed 25 July 2020].
• European Centre for Disease Prevention and Control. 2020. Vaccines And Treatment Of COVID-19 . [online] Available at: <https://www.ecdc.europa.eu/en/covid-19/latest-evidence/vaccines-and-treatment> [Accessed 25 July 2020].
• Misra, S., 2012. Randomized double-blind placebo control studies, the "Gold Standard" in intervention-based studies. Indian Journal of Sexually Transmitted Diseases and AIDS , 33(2), pp. 131.
• The New York Times. 2021. Coronavirus Drug and Treatment Tracker [online] Available at https://www.nytimes.com/interactive/2020/science/coronavirus-drugs-treatments.html [Accessed 11 March 2020]
• The New York Times. 2021. Coronavirus Vaccine Tracker [online] Available at https://www.nytimes.com/interactive/2020/science/coronavirus-vaccine-tracker.html [Accessed 11 March 2020]
• Wang, Y., Zhang, D., Du, G., Du, R., Zhao, J., Jin, Y., Fu, S., Gao, L., Cheng, Z., Lu, Q., Hu, Y., Luo, G., Wang, K., Lu, Y., Li, H., Wang, S., Ruan, S., Yang, C., Mei, C., Wang, Y., Ding, D., Wu, F., Tang, X., Ye, X., Ye, Y., Liu, B., Yang, J., Yin, W., Wang, A., Fan, G., Zhou, F., Liu, Z., Gu, X., Xu, J., Shang, L., Zhang, Y., Cao, L., Guo, T., Wan, Y., Qin, H., Jiang, Y., Jaki, T., Hayden, F., Horby, P., Cao, B. and Wang, C., 2020. Remdesivir in adults with severe COVID-19: a randomized, double-blind, placebo-controlled, multicentre trial. The Lancet , 395(10236), pp. 1569-1578.
• Winchesterhospital.org. 2020. Double-Blind Study . [online] Available at: <https://www.winchesterhospital.org/health-library/article?id=21861> [Accessed 25 July 2020].
• WHO Ad Hoc Expert Group on the Next Steps for COVID-19 Evaluation. 2021. Placebo-Controlled Trials of Covid-19 Vaccines — Why We Still Need Them. N Engl J Med, 384:e2.

Last Updated: Mar 19, 2021

Sara Ryding

Sara is a passionate life sciences writer who specializes in zoology and ornithology. She is currently completing a Ph.D. at Deakin University in Australia which focuses on how the beaks of birds change with global warming.

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Frequently asked questions

What is the difference between single-blind, double-blind and triple-blind studies.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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• Double Blind Studies in Research: Types, Pros & Cons

In the medical field, it is unethical to not inform your patient of a process or a procedure you want to carry out on them. It is required that the patients are informed about the treatment they would be given and that they consent to it. 

However, there is a method known as the blind study in psychological research.  A blind study prevents the participants from knowing about their treatment to avoid bias in the research.

This article will focus on the double-blind study which is a type of blind study which leaves both the researcher and the participants in the dark about important details of the study . That way the research is expected to be bias-free and far from any external influence.

The blind study has no ground in patient-doctor physical therapy sessions, but it is very helpful in other studies such as pharmacological research.

This is why we will consider a double-blind study, its usefulness, advantages, and disadvantages in a study or research. 

What is a Blinded Study?

A blinded study is research conducted in a way that prevents the subjects ( blind the subjects) from knowing the treatment they are given so that the researcher is guaranteed a biased free result. Information that can influence the subjects of a research is withheld from the subjects until the completion of the research.

If good blinding is carried out on the subjects, it can eliminate any form of biases that may arise from the subjects’ expectations, influence from the researcher, researcher’s bias , and other forms of biases that may occur in a research test.

This can be achieved as a blind study can be imposed on all participants in research. From the researcher to the subjects, the analysts, and even the judges or evaluators.

Free to use: Participant Consent Form Template

In some cases, however, imposing blind study in research may be impossible or even unethical. For example, it is unethical for a medical practitioner to blind a patient from knowing their treatment. The ethical thing to do is let your patient be informed about a major part of their treatment if it’s in a face-to-face intervention.

A subject can become unblinded during a study if they obtain information that has been previously shielded away from them. For example, if due to experiencing some side effects symptoms, a subject could correctly guess the treatment he/she has been exposed to. The subject then becomes unblinded. Subjects becoming unblinded mostly occur in pharmacological testings. 

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What is a Double-blind Study?

Double-blind refers to a study or research where both the subjects or participants of a study and the researchers are oblivious of the treatment being given and the subjects receiving the treatment. Both the participants and the experimenter are kept in the dark. This is done to eliminate all presence of biases in the outcome of the research.

It is most useful in research because of the placebo effect.

For example, if a researcher wants to conduct research on the effects of a newly introduced drug . A double-blind study requires that both the researcher and the subjects are unaware of the process.

So the researcher that is analyzing the subjects would have no information about the subjects receiving the new drug (which is the treatment group) and those who are not receiving the drug (which is the control group).

Now if the participants are not aware of their treatment and the researcher is not provided with information on who is receiving the treatment, the question that requires an answer is, why is a double-blind study needed?

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Purpose of a Double-blinded Study

Every procedure has its purpose and a double-blind study is not left out. The purpose of a double-blind study is to make sure that the outcomes of a study are free from biases. Using the double-blind method in a study improves the level of credibility and validity of the study 

A double-blind study is used in the scientific field, psychologists, and also in the legal process.

Read more: What are Cross-Sectional Studies: Examples, Definition, Types

Types of Blinded Studies

There are three types of blind studies namely single-blind study, double-blind study, and triple-blind study

1. Single-blind study : in this type of blind study only the subjects in the experiment are prevented from knowing the treatment they are given. The single-blind study is also known as the single masked study.

2. Double-blind study:   In the double-blind study both the subjects or participants and the researcher are blinded.  The researcher is unaware of who is receiving what treatment and the participants are unaware of the treatment they are receiving.

3. Triple blind study : here in the triple-blind study the participants, the researcher analyzing data , and the data collector are blinded from the information about the study. These three groups are prevented from knowing the treatments being given out or being received.

When to Use Each Type of Blind Study

Now that we know the types of blind study we are going to consider when it is appropriate to use either of these types of blind study in research.

• When to use a single-blind study

A single-blind study is usually conducted to prevent the subject from being aware of the treatment being studied. This is in case they get influenced and that leads to bias in the outcome.

It should be noted that there are cases where blinding a participant or patient is considered unethical. Therefore, single-blind study should only be used in statistical research or studies that don’t involve physical therapy between a patient and a doctor.

• When to use a double-blind study

Double-blind study is conducted when both the participants and the researcher are not allowed to know details of the research. This process is used to prevent bias in the study results and when there is a need to understand the characteristics of the results or to understand placebo effect.

• When to use a triple-blind study :

Use triple blind study if you aim to reduce your study and improve the accuracy of your results. This is because a triple-blind study allows randomization where the treatment item and the intervention are not known to the participants, researcher, data collector , or clinical personnel.

Read: Survey Errors To Avoid: Types, Sources, Examples, Mitigation

Advantages of Double-blinded Study

The following are the advantages of double-blind study:

1. It tests for three groups

The double-blind study usually involves three groups of subjects. The first is the treatment group, then the placebo, and lastly, the control group. The treatment group and the placebo are given the test item even though the researcher wouldn’t know which group is getting the treatment. No test item is administered to the control group because they are used as a basis of comparison for the results of the treatment group and the placebo.

If there’s a significant improvement in the placebo group over the control group, then it is considered that the treatment administered worked.

2. Reduces experimental bias

A double-blind study reduces the risk of biases in research. Biases can occur when a researcher influences the outcome of a study directly or otherwise. However, because the researcher is often also in the dark, it is difficult to influence the study.

This allows for credible, reliable, and valid research results.

3. Result duplication

The results of a double-blind study can be duplicated and that is why this procedure is considered one of the best practices. A double-blind study allows other researchers to follow up with the same processes, apply the test item, and compare the result with the control group.

The usefulness of this method is that if the results from these studies are close, it proves the validity of the test item that was administered. If there is no duplication in the research results, another study has to be carried out to determine why.

Disadvantages of a Double-Blind Study

1. it is expensive.

One huge disadvantage of a double-blind study is that it is expensive to conduct. It takes several months or years to complete because the researcher has to examine all the possible variables and they may have to use different groups to gather enough data. 

Many corporations after estimating the cost of this study which runs into millions of Dollars might have to spread the research across multiple months. Even for government studies, conducting this study may run into billions of dollars thereby making the medicine expensive in the market. This is one of the reasons why new prescription medicines are sold at an expensive price in the market.

2. Low representation

A double-blind study cannot provide a properly represented sample group because it is small. Most double-blind study is designed to enroll at least 100 people or participants for the research however the most preferable number is 300. It is true that the effectiveness of a treatment can be proven even in small studies but more people or participants are required to determine a pattern in research so that the results can be properly analyzed and verified.

Research generally requires participants in large numbers to participate in the trials and progress of a treatment being administered or in plan to be introduced to the market.

This is because even when the product or treatment item has gotten to the third phase of testing it still has only a 60% chance to proceed to another stage.

3. Negative reaction

In some cases some of the participants may react negatively to the treatment item when this happens the results from the test can be compared to see what changed. Some participants may react negatively to the placebo which may lead to producing some side effects that may make it seem like they were receiving the treatment item when they did not.

4. Time factor

Many times it is almost impossible to complete a double-blind study. For example, you cannot keep the subject or participants of a psychotherapy experiment in the dark about who gets the treatment item and who doesn’t get the treatment item. Double-blind study can only work in this scenario if you find a way to provide two similar procedures without each of the groups communicating about which group is getting the treatment item and which group is getting the placebo.

Frequently Asked Questions about Blind Studies

• Which is better: single-blind or double-blind study?

To determine which is best between a single-blind study and a double-blind study the case being studied has to be considered.

For example, if a researcher is conducting a study on the effects of a medicine that can cure Alzheimer’s, it is best to use a double-blind study rather than a single-blind study. This is because the participants will be unaware if they received the treatment item from the real drug or if they received the placebo which in turn reduces any external influence on the results of the test.

• When would you use a single-blind study?

Use a single-blind study if the participants having knowledge of the group they belong to might result in bias. I.e. whether their being aware of the treatment item and the questions of the study might result in bias.

• What is the difference between a single and double-blind study?

The significant difference between a single study and a double-blind study is that in a single-blind study only the participants or the patient are blinded while in a double-blind study both the participant and the researcher are blinded.

In any study, it is good to know how the results of the treatment group and the response group compare in an experiment. This is why a double-blind study is important. 

The risk of anyone manipulating data or influencing the participants is averted since a double-blind study prevents both the researcher or the participants from obtaining in-depth knowledge of the study.

You can be assured that the researcher cannot accidentally communicate with the subjects or participants. Now that is one huge importance and psychological benefit of the placebo effect.

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Module 4 - Epidemiologic Study Designs 1:

Cohort studies & clinical trials.

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Randomization and Blinding (Masking)

Randomization.

If assignment is truly unpredictable, then there is no bias in assignment, and neither the subjects nor the investigators can influence assignment. In addition, randomization of a large number of subjects tends to result in groups that differ only in treatment and are comparable with respect to all other factors and characteristics that might influence the outcome. As a result, randomization is the best method for eliminating confounding.

Blinded (or "masked" ) studies are those in which the subjects, and possibly the investigators as well, are unaware of which treatment the subject is receiving, e.g., active drug or placebo. Blinding is particularly important in drug trials when the study is assessing subjective outcomes , such as relief of pain or anxiety.

It isn't always possible to mask the treatments. For example, subjects randomly assigned to follow either a specific exercise regimen or continue their usual level of activity cannot be blinded.

• Single-blinded: the subjects are unaware of which group they have been assigned to.
• Double-blinded: Neither the subjects nor the investigators are aware of the treatment assignment until the end of the trial.

A placebo is an inert substance identical in appearance to the active treatment. Its purpose is to facilitate blinding by making the groups as similar as possible in the perception of treatment and to promote compliance. In the Physicians' Health Study participants were given a blister pack for each month (shown in the image below) that contained white tablets and red capsules that were taken on alternate days. The white tablets contained either 325 mg. of aspirin or an identical-looking inert substance; the red capsules contained either beta-carotene or an inert substance. The use of monthly blister packs also made it easier for participants to keep track of whether they had taken the correct pill each day.

It is not always ethical to use a placebo. If there is already a standard treatment or method of care, it would be unethical to withhold it. A new treatment should be compared to the standard therapy rather than to a placebo.

Example of Placebo Use to Achieve Blinding:

Glucosamine and chondroitin are naturally occurring substances that are structural components of the cartilage that lines our joints. Health food stores began selling supplements to people as a prevention (or treatment) for osteoarthritis despite a lack of evidence of their benefit in humans. Clegg and colleagues conducted a double-blind, randomized clinical trial in 1583 subjects with symptomatic osteoarthritis of the knee. Participants were randomly assigned to one of five treatment arms in order to test the efficacy of glucosamine and chondroitin. The primary outcome was greater than 20% decrease in total score on the WOMAC pain scale from baseline to week 24. Some of their results are shown in the table below.

Data from Clegg DO, et al.: Glucosamine, chondroitin sulfate, and the two in combination for painful knee osteoarthritis. N Engl J Med 354:795, 2006.

Perhaps the most remarkable observation is the response in the group treated with the placebo which had a cumulative incidence of >20% pain relief of 60% (188/313 = 0.60 = 60%)! This is an example of the "placebo effect" in which patients who perceive they are being treated often report subjective improvement, even if the treatment has no effect. Placebos make the perception of treatment similar among groups and provide a reference group that takes into account the placebo effect. Note also that the group treated with glucosamine and chondroitin had only a slightly greater response rate of 67%.

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Content ©2021. All Rights Reserved. Date last modified: September 1, 2021. Wayne W. LaMorte, MD, PhD, MPH

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Open Access

Study Protocol

Assessment of the pre-emptive effect of photobiomodulation in the postoperative period of impacted lower third molar extractions: A randomized, controlled, double-blind study protocol

Contributed equally to this work with: Daniel Rodríguez Salaberry, Anna Carolina Ratto Tempestini Horliana, Raquel Agnelli Mesquita Ferrari, Kristianne Porta Santos Fernandes

Roles Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Resources, Visualization, Writing – original draft, Writing – review & editing

Affiliations Postgraduate Program in Biophotonics Medicine, Nove de Julho University, Sao Paulo, Sao Paulo, Brazil, Dentistry School, Universidad Católica del Uruguay, Montevideo, Montevideo, Uruguay

Roles Data curation, Methodology, Project administration, Resources

¶ ‡ LHB, RWCC, PLL, MCC, RSN, MLLC, APTS, TG, CCGD, LJM, and SKB, also contributed equally to this work.

Roles Data curation, Methodology, Resources

Roles Methodology, Visualization

Affiliations Postgraduate Program in Biophotonics Medicine, Nove de Julho University, Sao Paulo, Sao Paulo, Brazil, Postgraduate Program in Aging Science, São Judas Tadeu University, Sao Paulo, Sao Paulo, Brazil

Roles Resources

Affiliation Postgraduate Program in Biophotonics Medicine, Nove de Julho University, Sao Paulo, Sao Paulo, Brazil

Roles Visualization

Affiliation Postgraduate Program in Bioengineering, Brasil University, Sao Paulo, Sao Paulo, Brazil

Roles Formal analysis, Visualization

Affiliation Postgraduate Program in Health and Environment, Metropolitana de Santos University, Santos, Sao Paulo, Brazil

Roles Conceptualization, Project administration, Supervision, Writing – original draft, Writing – review & editing

Roles Writing – original draft, Writing – review & editing

Roles Funding acquisition, Resources, Writing – original draft, Writing – review & editing

Affiliations Postgraduate Program in Biophotonics Medicine, Nove de Julho University, Sao Paulo, Sao Paulo, Brazil, Postgraduate Program in Rehabilitation Sciences, Nove de Julho University, Sao Paulo, Sao Paulo, Brazil

Roles Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

Roles Funding acquisition, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

Roles Conceptualization, Data curation, Funding acquisition, Methodology, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

• Daniel Rodríguez Salaberry, 
• Laura Hermida Bruno, 
• Rolf Wilhem Consolandich Cirisola, 
• Priscila Larcher Longo, 
• Maria Cristina Chavantes, 
• Ricardo Scarparo Navarro, 
• Marcela Letícia Leal Gonçalves, 
• Ana Paula Taboada Sobral, 
• Thais Gimenez, 

• Published: June 17, 2024
• https://doi.org/10.1371/journal.pone.0300136
• Reader Comments

Photobiomodulation is a safe option for controlling pain, edema, and trismus when applied postoperatively in third molar surgery. However, administration prior to surgery has been under-explored. This study aims to explore the effectiveness of pre-emptive photobiomodulation in reducing postoperative edema in impacted lower third molar extractions. Two groups of healthy individuals undergoing tooth extraction will be randomly assigned: Control group receiving pre-emptive corticosteroid and simulated photobiomodulation, and Photobiomodulation Group receiving intraoral low-intensity laser and extraoral LED cluster application. The primary outcome will be postoperative edema after 48 h. The secondary outcomes will be pain, trismus dysphagia, and analgesic intake (paracetamol). These outcomes will be assessed at baseline as well as two and seven days after surgery. Adverse effects will be recorded. Data will be presented as means ± SD and a p-value < 0.05 will be indicative of statistical significance.

Citation: Salaberry DR, Bruno LH, Cirisola RWC, Longo PL, Chavantes MC, Navarro RS, et al. (2024) Assessment of the pre-emptive effect of photobiomodulation in the postoperative period of impacted lower third molar extractions: A randomized, controlled, double-blind study protocol. PLoS ONE 19(6): e0300136. https://doi.org/10.1371/journal.pone.0300136

Editor: Michael R. Hamblin, Massachusetts General Hospital, UNITED STATES

Received: November 12, 2023; Accepted: February 20, 2024; Published: June 17, 2024

Copyright: © 2024 Salaberry et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data resulting from its development will be stored in an anonymized manner at the Open Science Framework repository. The files will be posted on the already created link ( https://doi.org/10.17605/OSF.IO/3EVMR ).

Funding: KPSF, SKB, RAMF and ACRTH were supported by CNPq- National Council for Technological and Scientific Development (CNPq, grant n. 304330/2020-5, n. 306577/2020-8, n.310491/2021-5 and n. 314668/2020-9). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The extraction of impacted lower third molars is a very common procedure that has been technically standardized since the early 20 th century [ 1 ]. However, it remains associated with a high degree of postoperative discomfort, primarily related to pain, swelling, trismus, and dysphagia [ 1 , 2 ].

To minimize these unpleasant effects on patients’ quality of life, various pharmacological and non-pharmacological measures with varying degrees of clinical effectiveness have been recommended [ 1 , 3 ]. Pharmacological measures include the use of analgesics, antibiotics, and anti-inflammatory drugs, primarily corticosteroids [ 1 , 3 – 7 ]. Some authors have suggested the pre-emptive use of anti-inflammatory drugs for controlling postoperative pain and swelling following third molar extractions [ 8 – 11 ]. However, there is no definitive evidence with regards to the benefits of prophylactic use, which has been associated with numerous side effects, such as hyperglycemia, insulin resistance, the development of glucocorticoid resistance, hypertension, muscle atrophy, lipid deposition abnormalities, interference with normal healing, an increased risk of infection, mood disorders, cardiovascular complications, and osteoporosis [ 12 – 14 ]. Therefore, the restriction of the use of such drugs is essential, especially in prolonged periods [ 14 , 15 ]. Non-pharmacological resources include cryotherapy, ozone therapy, growth factors, and photobiomodulation [ 1 , 16 ].

Photobiomodulation has proved to be a viable option when applied postoperatively in third molar surgeries to control pain, swelling, and trismus, both with wavelengths in the red and infrared spectrum when used separately [ 17 , 18 ]. Thus far, however, the combined use of wavelengths in both ranges of the light spectrum prior to surgery has not been sufficiently investigated [ 19 , 20 ]. Given the promising results demonstrated by photobiomodulation, probably the preoperative application of red and infrared laser and LED (both extraoral and intraoral) is expected to be effective in reducing postoperative swelling in retained lower third molar extractions.

Thus, the aim of the proposed study is to investigate the effectiveness of pre-emptive photobiomodulation in reducing post-operative edema following the extraction of impacted lower third molars.

Study design

This randomized, controlled, double-blind clinical protocol is in accordance with the criteria for clinical study design as per the SPIRIT Statement. It has been submitted to the Human Research Ethics Committee of the Catholic University of Uruguay (process: 220420). After a verbal and written explanation of the study, volunteers who agree to participate will sign a statement of informed consent. All patients will have the option to withdraw from the study at any time. The study will be conducted in accordance with the Declaration of Helsinki. The treatments will be carried out at the Dental Surgical Clinic of the University Health Clinic (Apolônia) at the Catholic University of Uruguay from November 2023, to July 2024. Any complications or changes will be investigated and reported to the Research Ethics Committee. The project has been registered on the Clinical Trials platform ( www.clinicaltrial.gov ), registration number: NCT05924191. The data resulting from its development will be stored in an anonymized manner at the Open Science Framework repository. The files will be posted on the already created link ( https://doi.org/10.17605/OSF.IO/3EVMR ).

Sample size calculation

The sample size was calculated to provide an 80% power (α = 0.05). The sample size will consist of 30 surgeries per group. Thirty patients with bilateral and symmetric lower third molars (right and left) will be selected. The procedure will then be performed randomly (split-mouth design), totaling 60 surgeries (30 surgeries in the control group and 30 surgeries in the experimental group). For the calculation, we used the 48-hour time point in the article by Aras and Güngörmüş (2010) [ 21 ]. The average edema measurement was 105 mm in the control group and 109 mm in the experimental group. The larger standard deviation between the two means ±5 mm was chosen for the sample size calculation.

Calibration and examiner training

The examiner who will conduct the pre-operative measurements (considered the gold standard) will lead the calibration process for the researchers who will perform the post-operative assessments. Joint training and calibration exercises will be carried out three times, and the data will be discussed among these researchers and the gold standard examiner to achieve an acceptable level of agreement using the Intraclass Correlation Coefficient (ICC) test [ 22 ]. Subsequently, the examiners will individually perform the proposed assessments in the study on 10 adult volunteers at different times. These patients will not be part of the sample. Reliability will be determined using the intraclass correlation coefficient (ICC) [ 22 ].

Laser’s safety methodology

A course conducted by qualified professionals will provide comprehensive laser safety training for all researchers, ensuring the safe use of lasers. The laser devices in this study are Class 3b (power varies between 5–500 mW). This category of lasers can cause accidental harm if looked at directly. All participants and researchers will use protective eyewear during the applications and simulations.

Participant characterization

Participants referred to the Oral and Maxillofacial Surgery and Traumatology service of the Catholic University of Uruguay, who need to undergo the extraction of impacted lower third molars, will be assessed. The selection of cases will be based on the surgical difficulty of the procedure and anatomical position. The molars will be classified according to Pell and Gregory Classes II or III and/or B or C; vertical or mesioangular position, following Winter’s classification, or Class II with the need for osteotomy, or Class III with the need for osteotomy and tooth sectioning, based on the Prant Scale modified by Amarillas-Escobar et al (2010) [ 23 ], which is used to assess the difficulty of the dental extraction procedures.

Individuals eligible for inclusion — will need to have two impacted lower third molars, with inclusion based on the surgical difficulty of the procedure and anatomical position as described in the Participant Characterization section. There should be a clear indication for third molar extraction, whether due to recurrent infections, malposition, orthodontic recommendation, or a professional recommendation provided in writing. Male and female individuals between 18 and 50 years of age without any comorbidities will be eligible. The individuals should also maintain good oral hygiene to be considered for the study.

Exclusion of individuals—if they have local conditions that contraindicate surgical intervention or could complicate the post-operative period, such as acute pericoronitis in the previous 30 days or temporomandibular joint ankylosis. Smokers, individuals lacking both upper and lower central incisors, those with a history of photosensitivity, pregnant or lactating women, and individuals using anti-inflammatory drugs or analgesics and those with allergies to any drugs used in the study (e.g., paracetamol, 2% chlorhexidine, local anesthetics, sodium bisulfite) will also be excluded. Individuals experiencing surgical complications, such as bleeding or operative difficulties, will be ineligible for the study due to the potential impact on maintaining the expected surgical standards and the accuracy of the study outcomes. Participants undergoing surgical procedures lasting longer than 90 minutes will be excluded from the study. Surgeries lasting longer than 90 minutes could potentially impact the accuracy and reliability of the findings due to the likelihood of increased post-operative trauma, which may result in more significant edema and pain.

Randomization

We will use a random sequence generator program ( https://www.sealedenvelope.com/ ) to assign participants to the two experimental groups. Randomization will involve 30 participants. Each participant will undergo two surgeries–one on each side. The first surgery will be performed on the right side and treatment for the left side will be the opposite of the randomized treatment, ensuring no repetition of identical treatments in the same patient. A third party not otherwise involved in the study will handle sequence generation and envelope preparation. Opaque envelopes labeled with sequential numbers will contain a sheet specifying the group for the first surgery based on the generated sequence. These envelopes will remain sealed and stored securely in numerical order until the surgeries.

The patients will undergo assessments by the surgeons and, upon meeting the previously described eligibility criteria, will be enrolled in the study. All participants will be submitted to the same surgical protocol. Just prior to the surgeries, the researcher responsible for photobiomodulation application will open an envelope (without changing the sequence of the remaining envelopes) and perform the procedure indicated.

Blinding of the study

Only the researcher responsible for administering the treatments (who will open the randomization envelopes) will know which treatment is assigned to each side of the patient. Group identities will be revealed after the statistical analysis of the data to all those involved in the study. Therefore, the researcher responsible for data collection, patient, and statistician will be blinded to the treatments assigned to the groups.

Surgery will be performed conventionally, with the LED/laser turned off in the control group. However, the same points will be treated in the pre-operative (baseline) period as in the experimental group. To prevent the patient from identifying non-activation based on sound of the device, the sound will be recorded and played during the simulation. This blinding method ensures that the participants, data collectors, and statistician remain unaware of the treatment assignments throughout the study. The main investigator will obtain informed consent from potential trial participants. Procedures for collecting, sharing, and safeguarding personal information of potential and enrolled participants will be implemented to ensure confidentiality throughout the trial, both during and after its completion.

Preoperative assessment

All participants who meet the eligibility criteria for the study and sign the statement of informed consent will undergo a preoperative assessment, which will involve the initial measurement (baseline, pre-surgery) of all study outcomes. These assessments will be conducted by a trained researcher for objective outcomes and a calibrated researcher for subjective outcomes. Surgery will proceed as usual, similar to conventional third molar surgery.

Surgical procedures

One hour after photobiomodulation (t = 0), the participants will undergo the removal of the third molar by a surgeon following the standard technique at the Catholic University of Uruguay.

A local anesthetic will be administered to the inferior alveolar, lingual, and buccal nerves using two 1.8 ml carpules containing 2% mepivacaine with epinephrine 1:100,000 (MEPIADRE 100, DFL, Brazil). An intrasulcular incision will be made with a No. 15 scalpel blade (BIOSET, China) on the buccal side of the second lower molar, extending distally and laterally toward the anterior edge of the mandibular ascending ramus. A triangular periosteal mucosal flap will be dissected using a Molt No. 9 periosteal elevator (HU-FRIEDY, USA).

The buccal and distal bone will be removed using a No. 8 round tungsten carbide bur (STRAUSS AND CO., Israel) and low-speed surgical micromotor (BIOMET 3i, OSSEOCISION NE 111, Japan), with abundant irrigation using 0.95% saline solution. If necessary, coronal and/or interradicular tooth sectioning will be performed using a No. 703 tungsten carbide bur (STRAUSS AND CO., Israel) at low speed and with constant irrigation.

After extraction, thorough cleaning of the residual surgical cavity will be performed, followed by repositioning, and suturing the mucosal flap with 3–0 silk thread (BESTCARE, Portugal) mounted on a disposable circular-section needle (BESTCARE, Portugal).

Medication protocols in the preoperative period

Control Group: Dexamethasone 8 mg, 1 tablet administered 1 hour before surgery.

Experimental Group: Photobiomodulation applied intraorally and extraorally 1 hour before surgery (detailed in the "Photobiomodulation method" section).

Medication protocols in the postoperative period (both groups):

Amoxicillin 750 mg tablet, orally every 12 hours for 7 days.

Anti-inflammatory medication: Ketorolac 10 mg every 6 hours for 3 days.

Analgesic medication: Paracetamol 500 mg every 6 hours for 3 days to be taken in the occurrence of pain (number of tablets taken will be quantified as a secondary outcome).

Experimental design

Prior to the surgical procedure, the researcher responsible for treatment will open an envelope (without altering the numerical sequence of the remaining envelopes) and perform the procedure indicated. The 30 individuals will be allocated to the experimental and control groups as follows ( Fig 1 ):

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

VAS—visual analog scale.

https://doi.org/10.1371/journal.pone.0300136.g001

Control Group (n = 30 surgeries)—Individuals will receive conventional treatment with dexamethasone 8 mg orally 1 hour prior to surgery [ 13 ] + simulated photobiomodulation. The laser will be turned off and will be applied at the same points as in the experimental group in the immediate preoperative period (baseline).

Experimental Group (n = 30 surgeries) — Participants will receive photobiomodulation 1 hour prior to the surgical procedure + a placebo tablet of dexamethasone (Laboratory Matías Gonzalez, Montevideo).

Photobiomodulation method—All participants and researchers will wear individual protective glasses during the applications and simulations.

Intraoral photobiomodulation—In the photobiomodulation Group, intraoral irradiations will be performed with a low-intensity laser at a wavelength of 660nm (power of 0.1W, radiant exposure of 1061 J/cm 2 , and energy of 3 J per point, with a 30-second duration at each point, totaling 12J). The same four points will also be irradiated using a wavelength of 808nm (power of 0.1W, radiant exposure of 1061 J/cm 2 , and energy of 3 J per point, with a 30-second duration at each point, totaling 12J), adapted from Sierra et al., 2015 [ 24 ].

Photobiomodulation method

All participants and researchers will use protective eyewear during the applications and simulations.

Intraoral photobiomodulation—In the photobiomodulation group, intraoral irradiation (4 points) will be performed with a low-intensity laser at a wavelength of 660 nm (power of 0.1 W, radiant exposure of 1061 J/cm 2 , and energy of 3 J per point, with a 30-second duration at each point, totaling 12 J). The same four points will also be irradiated using a wavelength of 808 nm (power of 0.1W, radiant exposure of 1061 J/cm 2 , and energy of 3 J per point, with a 30-second duration at each point, totaling 12 J), adapted from Sierra et al. (2015) [ 24 ].

Intraoral irradiation ( Table 1 ) will be performed by placing the laser head directly in contact with four points on the lingual mucosa in the surgical field (alveolus of the tooth to be extracted): At the site where the suture will be performed (middle of the bone socket), on the lingual surface (cervical third), on the lingual surface (middle third), and on the lingual surface (apical third).

https://doi.org/10.1371/journal.pone.0300136.t001

Extraoral photobiomodulation — The extraoral photobiomodulation will be applied using a cluster with a contact area of 20 cm 2 over the insertion point of the masseter muscle.

Extraoral photobiomodulation — Extraoral photobiomodulation will be applied using a cluster with a contact area of 20 cm 2 over the insertion point of the masseter muscle. The cluster will be of the brand Ibramed (Amparo, São Paulo, Brazil), model Antares (small cluster 2). Irradiation will be performed [ 21 , 25 ] using the cluster of 5 LEDs with a wavelength of 630 nm (power of 0.25 W per LED, radiant exposure of 3 J/cm 2 , and energy of 12 J, with a total time of 48 seconds, totaling 60 J of energy). The same region will then be irradiated with the cluster of 4 LEDs with a wavelength of 850 nm (power of 0.3W per LED, radiant exposure of 2.4 J/cm 2 , and energy of 12 J per point, with a total time of 40 seconds, totaling 48 J of energy). This protocol will be applied one hour prior to surgery ( Table 2 ).

https://doi.org/10.1371/journal.pone.0300136.t002

Individuals who experience any complications during the research period, such as allergic reactions to any of the materials used, an allergic reaction to Paracetamol®, or having taken any other drug not provided, those who, in any way, become unable to attend the appointments on the scheduled dates, relocate to a different city, or simply no longer wish to participate in the research will be excluded from the study but will not suffer any negative consequences in terms of treatment.

The data of all participants will be included in the statistical analysis, described, and discussed, along with any potential major and minor adverse effects.

Study outcomes

The primary outcome of the study will be:

Edema—three measurements will be taken on the patient’s face with a previously sanitized flexible measuring tape: tragus-pogonion, tragus-labial commissure, and mandibular angle-outer orbital commissure [ 26 ]. Measurements will be taken immediately before surgery as well as two and seven days after surgery.

The secondary outcomes of the study will be:

Pain Sensitivity — The visual analog scale (VAS) is the most widely used instrument for measuring postoperative pain after oral surgeries with photobiomodulation in the postoperative period [ 24 , 26 ]. In this study, the VAS will be used immediately after surgery as well as two and seven days after surgery [ 24 , 26 ] using 10-cm line with "0" (no pain) at one end and "10" (unbearable pain) at the other. Instructions on marking will be provided to the patient by the same operator.

Quantity of pain tablets taken during the period — The number of analgesic tablets taken at two and seven days will be counted. All participants will take paracetamol and be instructed to take it every six hours only in the presence of pain [ 27 ]. At the beginning of the study, each participant will be provided a paracetamol tablet, which is a drug with purely analgesic effects [ 28 ]. At the end of the experiment, the quantity of tablets will be evaluated as a secondary outcome. Patient adherence will be monitored. For such, the patients will be asked to bring the medication blister pack to the appointment to check how it is being used.

Postoperative trismus assessment — Spasms in the masticatory muscles (trismus) can limit or even prevent mouth opening after the surgical removal of impacted third molars [ 21 , 23 , 29 ]. This outcome is typically evaluated by measuring the distance between the incisal edges of the upper and lower central incisors using calipers [ 27 , 29 ]. In this study, previously calibrated evaluators will measure mouth opening in each patient before surgery as well as two and seven days after surgery.

Impact of the surgical procedure on quality of life — Two previously calibrated evaluators will ask patients to respond with "yes" or "no" to the following 10 questions two and seven days after surgery, as described by Sierra et al. (2013) [ 26 ] and Sierra et al. (2015) [ 24 ].

The 10 questions were:

Are you maintaining your social activities normally?

Are you working/studying normally?

Are you maintaining your regular diet?

Do you have difficulty swallowing due to the surgery?

Do you have difficulty tasting food?

Can you chew on the operated side?

Do you have difficulty sleeping due to the surgery?

Did you have difficulty speaking due to the surgery?

Has your appearance changed due to the surgery?

Do you experience nausea due to the surgery?

Dysphagia—The assessment of dysphagia will be conducted two and seven days after surgery through questions [ 27 ] and classification on a numerical scale: (0) total absence of dysphagia; (1) dysphagia for solid foods; (2) dysphagia for any liquid or solid food.

Assessment of the presence and intensity of hematoma/ecchymosis — The presence of hematoma/ecchymosis will be assessed by measuring the largest diameter of color changes on the skin of the cheek and submandibular region at two and seven days after surgery. The measurement will be performed by a previously calibrated evaluator, who will classify the occurrence of this event in four categories: (1) non-existent; (2) largest diameter less than 4 cm; (3) largest diameter between 4 and 10 cm; and (4) largest diameter greater than 10 cm, as described by Bjornsson et al. (2003) [ 30 ].

Statistical analyses

Initial descriptive analyses will be performed for all variables measured in the study, both quantitative (mean and standard deviation) and qualitative (frequencies and percentages). Normality tests will then be conducted to determine the appropriate statistical tests for specific analysis. Subgroup analysis will be performed: treatment x sex interaction [ 31 ]. A significance level of 5% probability or corresponding p-value will be adopted for all tests. All analyses will be conducted using the statistical software SPSS for Windows, version 9.1. Interim analysis was not planned.

For the management of pain and swelling following third molar extractions, some authors recommend the pre-emptive use of anti-inflammatories. However, conclusive evidence regarding the efficacy of pre-emptive use remains elusive. In a 2018 systematic review addressing optimal dosages and administration routes for corticosteroids in lower third molar extractions, indicated an absence of well-established standards for the ideal route, type, and dosage. Nonetheless, preoperative corticosteroid administration through submucosal injection demonstrated effectiveness in mitigating edema, pain, and trismus post-surgery (Larsen et al., 2018) [ 10 ]. In 2019, a meta-analysis found that methylprednisolone, irrespective of administration route, significantly alleviates immediate postoperative edema, albeit with no discernible impact in the subsequent days following lower third molar extractions. Oral administration or intra-masseter application appeared promising in reducing pain and trismus during the immediate postoperative period. Notably, oral administration demonstrated potential in mitigating late-phase pain, while intra-masseter administration effectively controlled trismus. Despite these findings, researchers underscored the imperative for additional controlled and randomized clinical trials to fortify evidence pertaining to methylprednisolone prescription in lower third molar extractions [ 12 ]. A 2019 systematic review with meta-analysis concluded that corticosteroids exhibit greater efficacy than placebos in managing pain and trismus associated with the postoperative period of lower third molar extractions, particularly when employed preventively or pre-emptively. The route of administration minimally influenced outcomes, except for the submucosal route. Continuation of corticosteroid use post-surgery did not yield additional positive effects in postoperative comfort [ 13 ]. Nevertheless, a 2021 systematic review with meta-analysis by Singh et al. [ 14 ] indicated that the use of systemic corticosteroids, even in oral surgeries involving trauma, rests on weak scientific evidence, necessitating further studies to substantiate its recommendation.

Photobiomodulation (PBM) has emerged as a viable alternative for postoperative management in third molar surgeries, effectively controlling pain, swelling, and trismus [ 17 , 18 ]. Two studies have highlighted the preventive efficacy of PBM, specifically with infrared wavelength, in lower third molar extractions [ 19 , 20 ]. However, the combined preoperative application of PBM has not been explored to date. Considering the positive outcomes observed with PBM, it is plausible that pre-emptive application using both red and infrared LED, both extraorally and intraorally, could prove effective in reducing postoperative swelling in lower third molar extractions. Consequently, PBM stands as a non-pharmacological alternative to enhance postoperative comfort following impacted lower third molar extractions.

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• 20. Mello ES. Preparation of orofacial tissues involved in surgeries for impacted lower third molars with infrared LED for pain, trismus, and edema control: A clinical trial, randomized, double-blind, controlled study. M.Sc. Dissertation. Nove de Julho University, 2021. Available from: https://bibliotecatede.uninove.br/bitstream/tede/2613/2/Erika%20da%20Silva%20Mello.pdf
• 22. Fleiss JL. Design and analysis of clinical experiments. New York: Wiley;1986.

• Introduction
• Conclusions
• Article Information

EGCG indicates epigallocatechin-3-gallate.

Positions of the squares in the forest plot show the estimate of the hazard ratio (HR) describing the relative effect of epigallocatechin-3-gallate (EGCG) compared with the placebo, with the 95% CI represented by the horizontal lines. Squares to the left of the vertical line indicate when the adverse effects rates were lower in the EGCG group compared with the placebo. A, In patients who were enrolled and eligible; B, in patients who had modified radical mastectomy only. STAT indicates Skin Toxicity Assessment Tool.

Error bars indicate SDs.

Trial Protocol

eFigure 1. Change in RID and symptom scores between epigallocatechin-3-gallate and placebo groups

eFigure 2. The comparison of different RID-related indexes of patients with modified radical mastectomy between two groups

eFigure 3. The representative cases in the epigallocatechin-3-gallate (panel A) and placebo groups (panel B)

eTable 1. Radiation Therapy Oncology Group score

eTable 2. The characteristics of patients who received modified radical mastectomy

eTable 3. The highest RID-related scores of patients who received modified radical mastectomy

eTable 4. The characteristics of patients who underwent skin temperature measurements

eTable 5. Adverse events

Statistical Analysis Plan

Data Sharing Statement

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Zhao H , Zhu W , Zhao X, et al. Efficacy of Epigallocatechin-3-Gallate in Preventing Dermatitis in Patients With Breast Cancer Receiving Postoperative Radiotherapy : A Double-Blind, Placebo-Controlled, Phase 2 Randomized Clinical Trial . JAMA Dermatol. 2022;158(7):779–786. doi:10.1001/jamadermatol.2022.1736

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Efficacy of Epigallocatechin-3-Gallate in Preventing Dermatitis in Patients With Breast Cancer Receiving Postoperative Radiotherapy : A Double-Blind, Placebo-Controlled, Phase 2 Randomized Clinical Trial

• 1 Department of Radiation Oncology and Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
• 2 Department of Breast Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
• 3 Second Affiliated Hospital of Chengdu Medical College (China National Nuclear Corporation 416 Hospital), Chengdu, China

Question   Does epigallocatechin-3-gallate (EGCG) reduce the occurrence of radiation-induced dermatitis (RID) for patients with breast cancer receiving adjuvant radiotherapy?

Findings   In this phase 2 randomized clinical trial of 180 patients, grade 2 or worse RID occurred in 50.5% of participants treated with EGCG solution and 72.2% with placebo, a statistically significant difference. Furthermore, symptom indexes were also significantly lower in patients receiving EGCG with a safety profile.

Meaning   The safety profile and prophylactic effect of topical EGCG solution may offer a convenient, well-tolerated, and valid option for patients with breast cancer who are at risk for RID.

Importance   Safe and effective prophylactic therapies for radiation-induced dermatitis (RID) remain an unmet need.

Objective   To determine if epigallocatechin-3-gallate (EGCG) solution reduces the incidence of RID in patients undergoing radiotherapy after breast cancer surgery.

Design, Setting, and Participants   This phase 2 double-blind, placebo-controlled randomized clinical trial enrolled 180 patients with breast cancer receiving postoperative radiotherapy at Shandong Cancer Hospital and Institute in Shandong, China, between November 2014 and June 2019. Data analysis was performed from September 2019 to January 2020.

Interventions   Participants were randomly assigned (2:1) to receive either EGCG solution (660 μmol/L) or placebo (0.9% NaCl saline) sprayed to the whole radiation field from day 1 of the radiation until 2 weeks after radiation completion.

Main Outcomes and Measures   The primary end point was incidence of grade 2 or worse RID, defined by the Radiation Therapy Oncology Group scale. The secondary end points included RID index (RIDI), symptom index, changes in the skin temperature measured by infrared thermal images, and safety.

Results   A total of 180 eligible patients were enrolled, of whom 165 (EGCG, n = 111; placebo, n = 54) were evaluable for efficacy (median [range] age, 46 [26-67] years). The occurrence of grade 2 or worse RID was significantly lower (50.5%; 95% CI, 41.2%-59.8%) in the EGCG group than in the placebo group (72.2%; 95% CI, 60.3%-84.1%) ( P  = .008). The mean RIDI in the EGCG group was significantly lower than that in the placebo group. Furthermore, symptom indexes were significantly lower in patients receiving EGCG. Four patients (3.6%) had adverse events related to the EGCG treatment, including grade 1 pricking skin sensation (3 [2.7%]) and pruritus (1 [0.9%]).

Conclusions and Relevance   In this randomized clinical trial, prophylactic use of EGCG solution significantly reduced the incidence and severity of RID in patients receiving adjuvant radiotherapy for breast cancer. It has the potential to become a new choice of skin care for patients receiving radiotherapy.

Trial Registration   ClinicalTrials.gov Identifier: NCT02580279

Breast cancer is the leading malignant neoplasm in women worldwide. 1 Radiotherapy is essential for patients with breast cancer who received mastectomy or breast-conserving surgery, to prolong local control time and overall survival. 2 - 4 The most common adverse event associated with breast cancer radiotherapy is radiation-induced dermatitis (RID). Even though RID is very rarely life-threatening, its bothersome symptoms may cause treatment interruptions and even earlier treatment terminations without the delivery of the prescribed total radiation dose, which might compromise the outcome. There is no recognized standard approach to prevent RID. 5 - 8 As stated in the guidelines of the Multinational Association of Supportive Care in Cancer Skin Toxicity Study Group, 9 the strongest evidence was from the Miller trial, 5 in which prophylactic mometasone showed only significant reduction in discomfort or burning and itching, not RID grade. More recently, the guidelines of the Oncology Nursing Society recommended the use of topical steroids to minimize RID, but strength of recommendation is conditional, and certainty of evidence is low. 10 An American Society for Radiation Oncology editorial also stated that the guideline authors found many flaws in the studies performed that limited the strength of the recommendations. 11 Therefore, more clinical strategies need to be established to prevent RID.

Epigallocatechin-3-gallate (EGCG), the major and most highly bioactive constituent in green tea, is responsible for its biochemical and pharmacological effects. 12 - 15 A retrospective study suggested that green tea extracts helped to restore the skin integrity in patients with grades 2 or higher RID receiving head and neck radiotherapy. 16 We performed a phase 1 dose-escalating trial for treating RID using EGCG in breast cancer radiotherapy, which demonstrated that the topical administration of EGCG solution is well tolerated, and the maximum tolerated dose was not found. 17 Subsequently, we conducted a single-arm phase 2 trial and demonstrated that topical EGCG reduced 71.4% to 89.8% of RID symptoms during adjuvant radiotherapy after modified radical mastectomy. 18 There was no study to investigate whether topical EGCG could prevent RID. Based on these encouraging results, we performed the current trial. The purpose of this placebo-controlled phase 2 randomized clinical trial was to investigate the prevention effects of EGCG solution vs placebo in patients with breast cancer who received postmastectomy adjuvant radiotherapy.

This trial was conducted at Shandong Cancer Hospital and Institute in Shandong, China. Eligibility criteria included women 18 years or older who had histologically confirmed breast cancer; with 2 to 3 weeks after the completion of adjuvant chemotherapy; Eastern Cooperative Oncology Group performance status of 0 to 1; adequate hematologic, hepatic, and kidney function profile; and no prior radiotherapy to the thorax. Exclusion criteria included patients with unhealed wounds in the radiation area; receiving other anticancer therapies except concurrent endocrine therapy or anti- ERBB2 (formerly HER2 ) therapy; with severe or uncontrolled medical conditions, pregnancy, or lactation; and a known allergy or hypersensitivity to EGCG (trial protocol in Supplement 1 ). There was no restriction regarding neoadjuvant or adjuvant chemotherapy regimens. The study protocol and informed consent were reviewed and approved by local institutional review and ethical committees. Written informed consent was provided by each participant. This study followed the Consolidated Standards of Reporting Trials ( CONSORT ) reporting guideline.

Considering the good effect of EGCG in previous studies, a 2:1 randomization (EGCG:placebo) was conducted based on patient benefit and adherence. 18 , 19 The allocation was performed by telephone with a computer-generated list, using a randomly permuted block design. Participants, investigators, and research staff assessing the participants were blinded to the treatment allocation. Just before the intervention, the investigator contacted the data center by telephone to receive the allocation group.

The simulation was performed by a big-bore spiral computed tomography (Philips Medical Systems) on BreastBoards (CIVCO Radiotherapy). Eclipse treatment planning system (Eclipse 8.6, Varian Medical Systems) was used for radiotherapy planning. The plan was designed using 6-MV beams to the chest/breast, supraclavicular area, and the internal mammary nodes according to N stage. Additional boluses were used in light of chest-wall thickness variation of all patients receiving postmastectomy radiotherapy. Patients with modified radical mastectomy received irradiation at a dose of 50 Gy in 25 fractions. Patients receiving breast-conserving surgery underwent simultaneous integrated boost (50-Gy whole-breast irradiation and 57.5-Gy tumor bed boost), sequential boost (50 Gy plus 10 Gy boost), or only 50 Gy whole-breast irradiation, at the physician’s discretion. The protocol allowed for dose variations between 95% and 105% of the reference point on the central axis. 20 , 21

The EGCG (95% purity by high-performance liquid chromatography) was purchased from HEP Biotech Co, Ltd, and its solution was freshly prepared (660 μmol/L). The application of EGCG solution or placebo (0.9% saline solution) was initiated from day 1 of radiotherapy until 2 weeks after radiotherapy completion. The EGCG or placebo solutions were uniformly sprayed on the whole radiation field using a sterilized medical sprayer, 3 times a day at 0.05 mL/cm 2 . The skin folds, such as the armpits, required a full stretch and exposure before spraying. The patients followed general good skin-care practices at the start of radiotherapy. No deodorant, lotion, cream, perfume, or any topical agent was allowed during radiotherapy.

Evaluation of RID was performed weekly by 2 investigators who were unaware of the patient’s clinical history and treatment allocation according to the Radiation Therapy Oncology Group (RTOG) scale (0 = no change; 4 = the worst impairment; eTable 1 in Supplement 2 ). 6 The grade is closely related to the degree of impairment. Higher grade indicates more severe impairment. Grade 2 or higher RID at any assessment time point during EGCG or placebo application is considered as having grade 2 or worse RID.

The RID grades for each time point also were plotted on a graph against time. The area under the curve, as RID index (RIDI), was calculated for each patient’s graph using the trapezoidal method. Therefore, RIDI are quantitative data, with higher score indicating longer duration of high-grade skin RID.

Patients reported RID-related symptoms (including pain, burning feeling, itching, pulling, and tenderness) using the Skin Toxicity Assessment Tool (0 = no symptom; 5 = the worst symptoms) once a week. 17 , 18 , 22 The highest score at each assessment time point was considered as having personal highest symptom score. A symptom score 2 or greater at any assessment time point was regarded as the symptom score of the individual patient greater than or equal to grade 2.

The same RIDI calculation method was used for the 5 RID-related symptom index. 19 The higher the index, the longer the severe symptoms persisted.

Skin temperature was measured weekly from March 2019 with the approval of the ethics committee owing to its ability to provide relatively objective measurement for skin injury. Difference in skin temperature (DST) was defined as the value of the irradiated breast or the chest wall minus that of the contralateral region. High-grade RID was associated with an additional increase in DST, which is probably associated with the intense inflammatory reaction. 23 , 24 The maximum increase in DST was determined by subtracting the baseline value from the maximum value during the entire observation period. A frontal thermal image of the torso from the neck to upper abdomen was obtained 2 hours before routine radiotherapy using a digital infrared thermal imager (FLIR E5 Serial No. 63985976).

Safety assessments were performed according to the National Cancer Institute Common Terminology Criteria for Adverse Events, version 4.0. Adverse events were monitored using laboratory tests, such as complete blood cell count, chemistry profile, and liver function tests. Chest computed tomography images were acquired at baseline and 1 month after the end of radiotherapy to evaluate lung toxicity.

Radiotherapy was interrupted in patients who developed grade 3 skin reactions characterized by confluent moist desquamation. Administration of EGCG or placebo was discontinued at the physician’s discretion. The wound dressing with normal saline was performed daily under sterile conditions, and antibiotics were given when necessary.

The primary end point was incidence of grade 2 or worse RID. The secondary end points included RIDI, the RID-related symptoms, the maximum increase in DST, and safety.

Based on documentations during RID prevention, 5 , 8 , 25 - 28 the incidence of grade 2 or higher RID observed during breast irradiation was about 75% in historical studies and 53% in topical steroids trials. According to the previous research experience of EGCG, 17 , 18 we hypothesized that its preventive effect should be no worse than that of topical steroids at least. To detect this difference with a power of 0.80 using a 2-sided test at significance level of .05, it was necessary to recruit 162 patients. Assuming a 10% attrition rate, a total of 180 patients would be required.

The RTOG scores, symptom scores, and safety assessments were recorded weekly from the start of radiotherapy to 2 weeks after its completion. The efficacy analysis set included patients in group and subgroup who completed the composite assessment by the investigators.

Statistical analyses were performed using SPSS Statistics for Windows, version 17.0 (SPSS Inc). Measurement data of the different groups are expressed as mean and SD and analyzed by t test. The qualitative measures were compared by the χ 2 test or Fisher exact test, as appropriate. Scores of 2 or more RID symptoms and their hazard ratios were calculated and compared with Mantel-Haenszel analysis. All the P values are 2-sided. The statistical analysis plan is detailed in Supplement 3 .

Between November 2014 and June 2019, 191 patients were screened, and 180 eligible patients were enrolled. Among them, 165 (EGCG, n = 111; placebo, n = 54) were evaluable for efficacy ( Figure 1 ). The characteristics of the fully eligible patients were similar between the 2 groups ( Table 1 ). The median (range) age of the 165 evaluable patients was 46 (26-67) years; 2 (1.2%) and 114 (69.1%) patients had smoking history and Eastern Cooperative Oncology Group performance status of 1, respectively.

The incidence of grade 2 or worse RID was significantly lower in the EGCG group at 50.5% (95% CI, 41.2%-59.8%) than that in the placebo group (72.2%; 95% CI, 60.3%-84.1%; P  = .008). There was a tendency that grade 3 or worse RID in the EGCG group was lower (4 of 111 [3.6%] vs 5 of 54 [9.3%]), but it did not reach statistical significance ( P  = .16). The mean (SD) RIDI of patients in the EGCG group was also significantly lower (5.22 [1.60]) than that in the placebo group (5.22 [1.60] vs 6.21 [1.56]; t  = −3.79; P  < .001). The highest scores of RID-related symptoms were also significantly lower after EGCG treatment compared with those of the placebo group at the end of the study, except with the pulling score ( Table 2 ). The incidence of grade 2 or worse burning feeling, itching, pain, and tenderness scores were all significantly lower in the EGCG group than in the placebo group ( Figure 2 A). Differences were statistically significant in all symptom indexes ( Figure 3 ).

The distributions of the maximum RID and symptom score for the 2 groups are detailed in eFigure 1 in Supplement 2 . As radiation dose increased, the average RID-related scores of patients in the EGCG group gradually reduced compared with those in the placebo group, and the difference reached the peak in the sixth week. For most patients, RID began to occur 2 to 3 weeks after the beginning of radiotherapy, but the mean (SD) appearance time of RID in the EGCG group was delayed (3.27 [0.86] weeks) compared with that in the placebo group (2.89 [0.60] weeks) ( P  = .001).

We conducted a subgroup analysis of patients with modified radical mastectomy (n = 137; eTable 2 in Supplement 2 ). The incidence of grade 2 or worse RID, burning feeling, itching, pulling, pain, and tenderness scores were all significantly lower in the EGCG group than in the placebo group ( Figure 2 B). The maximum scores of RID and radiation-induced symptoms were also significantly lower after EGCG treatment compared with the placebo (eTable 3 in Supplement 2 ). Differences were also statistically significant in RIDI and all symptom indexes (eFigure 2 in Supplement 2 ).

Of the 165 patients, 80 (48.5%; EGCG, n = 54; placebo, n = 26) underwent skin temperature measurements at baseline until 2 weeks after radiotherapy completion (eTable 4 in Supplement 2 ). The mean (SD) maximum increase in DST of 1.18 (0.73) (EGCG group) and 1.51 (0.99) (placebo group) showed no significant difference ( t  = −1.68; P  = .10). A representative patient per group, with thermography and photography weekly, is shown in eFigure 3 in Supplement 2 . The DST increased significantly as the radiotherapy progressed, as the thermography showed, and RID was also generally aggravated, as shown in the photograph. The RID score was the highest 1 week after the end of radiotherapy, and DST also reached the peak.

No severe adverse events were judged to be related to EGCG or placebo application (eTable 5 in Supplement 2 ). For the local skin of drug application, a total of 6 (3.6%) patients expressed discomfort within 10 minutes after drug application, which was considered to be related to radiotherapy and local drug application. Among them, 2 (3.7%) patients in the control group had grade 1 and grade 2 pricking skin sensation, respectively, and 3 (2.7%) patients in the EGCG group had grade 1 pricking skin sensation. One (0.9%) patient with EGCG application had grade 1 pruritus. The 2 symptoms were temporary and self-remitting and were not treated with other drugs.

Based on phases 1 to 2 clinical studies, 17 , 18 we conducted this phase 2 randomized clinical trial, which demonstrated that prophylactic use of EGCG solution significantly reduced the incidence and severity of RID in patients with breast cancer receiving adjuvant radiotherapy. 17 , 18 These findings may establish EGCG as an inexpensive and broadly available preventive agent for skin toxicity caused by ionizing radiation.

An increasing amount of clinical evidence provides guidance for RID prophylactic strategies. 5 - 7 The phase 3 RTOG 97-13 trial revealed that an emollient cream, Biafine, did not reduce the skin toxicity or improve the quality of life compared with best supportive care during adjuvant radiotherapy for breast cancer. 6 There is no evidence to support the prophylactic application of either the sucralfate or the aqueous cream tested for the prevention of radiation skin reactions. 7 Local steroids have some positive outcomes for the prevention of acute RID in patients with breast cancer treated with adjuvant radiotherapy. 5 , 29 However, some recent clinical trials from India and Japan had contradictory results in patients with head and neck cancer receiving radiation. These studies suggest that the effect of topical steroids is strongly advantageous for symptom management for RID, rather than prophylactic intervention. 30 , 31 Therefore, many experts state that there is currently no high-level evidence-based standard for RID. 32 Recently, some bioactive components extracted from natural plants have gradually stood out among many potential new radiation protective agents.

Green tea extract and its principal active ingredient, EGCG, are gaining attention with increased usage for radiation-induced damage, owing to their healthful properties. However, the molecular mechanisms underlying the beneficial effects of EGCG are complex. Treatment of human skin with EGCG inhibits UV radiation–induced oxidative stress. 13 Topical EGCG can protect human skin against UV-B–induced reactive oxygen species–associated inflammatory dermatoses, photoaging, and photocarcinogenesis. 14 Zhu et al 15 found that pretreatment with EGCG significantly enhanced the viability of the human skin cells that were irradiated with x-rays, with decreased apoptosis induced by x-ray irradiation. Use of EGCG induced the expression of the cytoprotective molecule heme oxygenase-1 in a dose-dependent manner via transcriptional activation.

Many sophisticated techniques are applied to breast cancer adjuvant radiotherapy. 33 In the past decade, different hypofractionated radiotherapy schemes were introduced, and patients receiving hypofractionated radiotherapy showed less skin toxicity compared with those receiving conventional fractionated radiotherapy. 34 To standardize the patient characteristics, the present study only enrolled patients receiving conventional fractional radiotherapy. Because hypofractionated radiotherapy is more widely used in whole-breast radiotherapy after breast-conserving surgery, fewer patients were enrolled in this study who received conventional fractionated radiotherapy; hence, further subgroup analysis was not performed. We plan to conduct further prospective studies in patients with breast cancer receiving hypofractionated radiotherapy to determine the effectiveness of topical EGCG.

In the process of developing the clinical preparations for the external use of EGCG, there are several important problems to be solved. First, its safety must be verified. A previous study showed that topical EGCG preparations caused minor dermal irritation in rats and guinea pigs, but not in rabbits, and was a moderate dermal sensitizing agent in the guinea pig maximization test. 35 We conducted a dose-escalating study for topical EGCG (140 μmol/L to 660 μmol/L) in breast cancer radiotherapy, which demonstrated that the topical administration of EGCG was well tolerated. 17 Because the maximum tolerated dose was not known, the highest dose in the phase 1 trial (660 μmol/L) was defined as the recommended dose in the phase 2 trial 18 and in the current study. In addition, it is necessary to evaluate the human skin penetration of EGCG in certain formulations. A previous study showed that EGCG can reach all the skin layers when the green tea extract was vehiculated in a cosmetic formulation. 36 Topical application of EGCG in hydrophilic ointment USP to human or mouse skin resulted in substantial intradermal uptake of up to 1% to 20% of the applied dose. 37 Freshly prepared EGCG saline solution was used in the present study, which might limit its actions. Ointment formulation is being developed to improve the skin penetration and intradermal uptake and facilitate further multicenter studies.

There are some limitations to the present study. First, the study was a single-center, phase 2 trial. The results need to be validated in multicenter phase 3 national or international trials. Second, not all the patients received infrared skin temperature measurement; therefore, the reduction in DST did not reach statistical significance. Thermography was reported in 2018 to be used for the evaluation of acute skin toxicity due to breast radiotherapy. 23 Our previous study also confirmed that RID was associated with thermographic response. 24 This approach needs to be further optimized to be more suitable as a clinical application.

In this randomized clinical trial, prophylactic use of EGCG solution significantly reduced the incidence and severity of RID in patients with breast cancer receiving adjuvant radiotherapy. Use of EGCG has the potential to become a new standard of skin care for patients receiving radiotherapy.

Accepted for Publication: April 5, 2022.

Published Online: June 1, 2022. doi:10.1001/jamadermatol.2022.1736

Corresponding Author: Ligang Xing, MD, PhD, Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Rd, Jinan, Shandong 250117, China ( [email protected] ).

Author Contributions: Dr Xing had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs H. Zhao and Zhu contributed equally to this work and share first authorship.

Concept and design: H. Zhao, Zhu, X. Zhao, Zhou, Xing, Yu.

Acquisition, analysis, or interpretation of data: H. Zhao, Zhu, X. Zhao, Li, Zhou, Zheng, Meng, Kong, Zhang, He.

Drafting of the manuscript: H. Zhao, Zhu, X. Zhao, Zhou, He.

Critical revision of the manuscript for important intellectual content: Zhu, X. Zhao, Li, Zhou, Zheng, Meng, Kong, Zhang, He, Xing, Yu.

Statistical analysis: Zhu, X. Zhao, Zhou, He.

Obtained funding: H. Zhao, Zhu, Xing, Yu.

Administrative, technical, or material support: H. Zhao, X. Zhao, Li, Zhou, Meng, Kong, Zhang, Yu.

Supervision: H. Zhao, Zheng, Yu.

Conflict of Interest Disclosures: None reported.

Funding/Support: Dr Zhu was supported by the National Natural Science Foundation of China (82003233), Jinan Science and Technology Plan Project (202019163), and Science and Technology Project of Traditional Chinese Medicine of Shandong Province (2021M013). Prof Yu was supported by the Academic Promotion Program of Shandong First Medical University (Shandong Academy of Medical Sciences) (2019ZL002) and the Innovation Project of Shandong First Medical University (Shandong Academy of Medical Sciences) (2019-04). Dr H. Zhao was supported by the Shandong Provincial Natural Science Foundation (No. ZR2016HM35).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Meeting Presentation: This work was presented in part at the Annual Meeting of American Society for Radiation Oncology; October 25-28, 2020; virtual.

Data Sharing Statement: See Supplement 4 .

Additional Contributions: We thank all patients and their families. We also thank the study investigators who participated in this study, especially the colleagues at the Department of Breast Surgery, the nurses at the Department of Radiation Oncology, and the staff in the Department of Clinical Research.

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(092) SEXUAL EXPERIENCES IN AN EXPLORATORY, PHASE 2B, RANDOMIZED, DOUBLE-BLIND, PLACEBO-CONTROLLED CLINICAL TRIAL OF SILDENAFIL, 3.6% CREAM FOR THE TREATMENT OF FEMALE SEXUAL AROUSAL DISORDER

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A Thurman, K Cornell, T Symonds, C Dart, J Hatheway, D Friend, A Goldstein, (092) SEXUAL EXPERIENCES IN AN EXPLORATORY, PHASE 2B, RANDOMIZED, DOUBLE-BLIND, PLACEBO-CONTROLLED CLINICAL TRIAL OF SILDENAFIL, 3.6% CREAM FOR THE TREATMENT OF FEMALE SEXUAL AROUSAL DISORDER, The Journal of Sexual Medicine , Volume 21, Issue Supplement_5, June 2024, qdae054.087, https://doi.org/10.1093/jsxmed/qdae054.087

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Quantifying the change in the mean number of satisfactory sexual experiences (SSEs) is an efficacy endpoint recommended by the US Food and Drug Administration in studies of treatments for female sexual dysfunctions (FSD), including female sexual arousal disorder (FSAD).

Post-hoc analysis of sexual experiences in a study of Sildenafil Cream, 3.6% for treatment of FSAD

Phase 2b, exploratory, randomized, placebo-controlled, double-blind study of Sildenafil Cream, 3.6% among premenopausal women with FSAD (NCT04948151). Following a baseline, single-blind, placebo, run-in period, participants were randomized and used investigational product (IP) at home for 12 weeks. Between monthly, in-clinic assessments, participants answered questions in an electronic diary (eDiary), within 24-hours of each sexual experience. Sexual experiences were categorized as “Partnered” versus “By Myself” and “Satisfactory” versus “Not Satisfactory”. eDiary question 11 (Q11) asked “Were conditions appropriate for satisfactory sexual activity (enough time, no distractions, etc.)?”. Changes from baseline in the mean number of SSEs was a secondary efficacy endpoint. The Orgasm domain (questions 22-24) of the Sexual Function Questionnaire (SFQ28) was an exploratory efficacy endpoint. Un-partnered women and women with sexual partners who did not sign informed consent were enrolled in the study, but in these cases (n=15), IP was only used in solo sexual experiences.

Sildenafil Cream users had more sexual experiences and more un-partnered sexual experiences during the double-blind treatment period compared to Placebo Cream users. Sildenafil Cream users were significantly more likely to engage in sexual experiences even when conditions were not appropriate for a satisfactory experience. Although, the change from baseline in the mean number of SSEs at week 12 decreased during solo, un-partnered sexual experiences for both product groups (-0.12±0.27 for Sildenafil versus -0.04±0.32 for Placebo, p=0.85), during the double-blind treatment period, Sildenafil Cream users reported significantly higher proportions of SSEs during un-partnered sexual experiences (197/261, 75.5%) compared to Placebo users (116/187, 62.0%) (p=0.002). Despite Sildenafil Cream users engaging in a sexual experience more frequently when conditions were not appropriate, the proportion of SSEs during these events when Q11=“No” were not different between active (17/59, 28.8%) versus placebo (8/25, 32.0%) users (p=0.77). Finally, un-partnered women and women with un-enrolled sexual partners, who by definition had only solo sexual experiences, had an increase from baseline in their SFQ Orgasm at week 12 with Sildenafil use (2.58±1.13) versus Placebo users in this subset, who had decreases in this score (-0.34±1.13) (p=0.13).

These data support that Sildenafil Cream 3.6% enhanced solo, un-partnered sexual experiences, which represented approximately 1 in 5 events in this FSAD treatment study.

Yes, this is sponsored by industry/sponsor: Dare Bioscience and Strategic Science and Technologies LLC

Clarification: Industry initiated, executed and funded study

Any of the authors act as a consultant, employee or shareholder of an industry for: Dare Bioscience and Strategic Science and Technologies LLC.

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• Published: 15 December 2023

Efficacy and safety of eptinezumab in patients with chronic migraine and medication-overuse headache: a randomized, double-blind, placebo-controlled study

• Shengyuan Yu 1 ,
• Jiying Zhou 2 ,
• Guogang Luo 3 ,
• Zheman Xiao 4 ,
• Anders Ettrup 5 ,
• Gary Jansson 5 ,
• Ioana Florea 5 ,
• Kristina Ranc 5 &
• Patricia Pozo-Rosich 6 , 7  

BMC Neurology volume  23 , Article number:  441 ( 2023 ) Cite this article

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For some people with migraine, despite taking greater amounts of acute headache medication (AHM), they develop an increase in monthly headache days. This cycle of increasing headache days, and in turn AHM use, can lead to a secondary headache disorder called medication-overuse headache (MOH). Preventive medications can prevent migraine from occurring and reduce reliance on AHMs, thereby preventing the cycle of MOH. This study was performed to evaluate the efficacy and safety of eptinezumab to prevent migraine/headache in a mainly Asian patient population with a dual diagnosis of chronic migraine and MOH.

SUNLIGHT was a phase 3, multicenter, double-blind, parallel-group, placebo-controlled trial. Patients aged 18−75 years with ≥ 8 migraine days/month and a diagnosis of MOH were randomly allocated (1:1) to one of two treatment groups: eptinezumab 100 mg or placebo. Monthly migraine days (MMDs) were captured using a daily electronic diary; the change from baseline in the number of MMDs over Weeks 1−12 was the primary efficacy endpoint.

Patients were randomized to eptinezumab 100 mg ( n  = 93) or placebo ( n  = 100). Over Weeks 1−12, eptinezumab reduced mean MMDs more than placebo (difference between treatments was -1.2; p  = 0.1484). Differences between treatment groups with p-values below 0.05 favoring eptinezumab were observed in 3 out of the 6 key secondary endpoints.

All endpoints numerically favored eptinezumab treatment when compared to placebo; however, this study did not meet its primary endpoint and is therefore negative. No new safety signals were identified in this study, like previous reports that confirmed the safety and tolerability of eptinezumab treatment.

Trial registration

ClinicalTrials.gov identifier: NCT04772742 (26/02/2021).

Peer Review reports

Migraine, a common and disabling neurologic disorder [ 1 , 2 ], is one of the leading causes of global disability [ 3 , 4 ]. Migraine, especially chronic migraine (CM; when a patient has headache [migraine-like or tension-type–like] on ≥ 15 days/month for ≥ 3 consecutive months, which on ≥ 8 days/month has the features of migraine headache) [ 5 ], can negatively impact all aspects of daily life and is associated with many comorbidities, including: major depression, anxiety, cardiac disorders, respiratory disorders, non-headache pain, and others [ 6 , 7 ]. Disease progression entails that some patients will develop an increase in monthly headache days (MHDs) despite taking greater amounts of acute headache medication (AHM). This cycle of increasing headache days and in turn more AHM use can lead to a secondary headache disorder called medication-overuse headache (MOH) [ 5 , 8 ].

Globally, MOH is estimated to occur in up to 70% of individuals with CM and is considered a risk factor for migraine chronification, where episodic migraine becomes CM [ 9 , 10 , 11 ]. In China, the prevalence of MOH within the migraine patient population is high, with hospital-based studies from 2013–2015 reporting that approximately 40–71% of CM patients also had MOH [ 12 , 13 ]. In Europe, patients with migraine and MOH generally constitute the most burdensome population, linked to approximately 86% of all healthcare costs generated by patients with headache disorders [ 14 ]. Moreover, patients with a dual diagnosis of CM and MOH often face the largest amount of burden and impact on quality of life [ 15 ].

Preventive migraine medication can prevent migraine attacks from occurring, reducing reliance on ineffective or poorly tolerated AHM and thereby breaking the cycle of MOH. However, there is a need for preventive medications that are more effective and better tolerated than the current standard of care [ 16 ]. The calcitonin gene-related peptide (CGRP) antagonist eptinezumab [ 17 ] is a peptide-binding IgG1 antibody that inhibits migraine onset; [ 18 ]; it has proven efficacy in adults with episodic migraine [ 19 ], with CM [ 20 ], and with 2–4 previous preventive migraine treatment failures [ 21 ]. Moreover, in subgroup analyses of patients with both CM and MOH, eptinezumab demonstrated efficacy in reducing monthly migraine days (MMDs), AHM use, and the impact of migraine as measured by patient-reported outcomes [ 8 , 22 , 23 ]. Key pharmacologic attributes of eptinezumab include high selectivity and affinity for CGRP, intravenous (IV) formulation, and short time to maximum plasma concentration (around 30 min) [ 24 , 25 ]. When compared to oral acute treatment(s), the IV route of administration avoids first pass metabolism in the liver and kidneys, allowing for a faster onset, which may be of high importance in this patient population. The objective of SUNLIGHT was to evaluate the efficacy and safety of eptinezumab to prevent migraine and headache in a primarily Asian patient population with the dual diagnosis of CM and MOH. Here, we report the primary results of the SUNLIGHT study and discuss factors potentially contributing to the study results.

Study design

SUNLIGHT was a randomized, double-blind, parallel-group, placebo-controlled phase 3 clinical trial that enrolled patients with a dual diagnosis of migraine and MOH for the purpose of evaluating the efficacy of eptinezumab within this specific demographic. This multicenter study was conducted in accordance with Good Clinical Practice standards as defined by the International Conference on Harmonisation and all applicable federal and local regulations. Each study site’s local review board or alternatively a central institutional review board/ethics committee approved all study documents. Patients were recruited from specialist settings in Mainland China, Republic of Korea, Taiwan, Spain, and Georgia. All patients provided written informed consent prior to their participation in the study. To view this registered study, see ClinicalTrials.gov under the following identifier: NCT04772742 (26/02/2021).

In this 36-week study, patients were followed through a screening period (28–30 days), a placebo-controlled period measuring efficacy (12 weeks), and an open-label period measuring safety and tolerability (12 weeks). For patients entering the open-label period, safety was followed for 20 weeks (12 weeks during the open-label period and 8 weeks during the safety follow-up period). These 20 weeks of safety data were analyzed and presented together. For patients not entering the open-label period, 20 weeks of safety follow-up data counted from when they received the first study drug infusion. The safety data collected up to Week 12 are thus included in the tabulations of the placebo-controlled period safety data, whereas the data collected at the safety follow-up visit for these patients are reported separately in data listings (Supplemental Figure 1 ). Patients were randomized to receive either eptinezumab 100 mg or placebo by IV infusion at the baseline visit. Eptinezumab (100 mg) was dispensed as 1 vial of 100 mg/mL concentrate for solution for infusion; 1 ml of 100 mg/ml concentrate for solution for infusion was added to 100 mL of 0.9% normal saline. Placebo was dispensed as 100 mL of 0.9% normal saline. The pharmacist or designee who received, stored, prepared, and dispensed eptinezumab and placebo IV infusions was unblinded and not involved in clinical study activities for which blinding was needed. The blinded investigator or designee intravenously administered study drug or placebo, which took approximately 30 min (± 15 min). At the primary outcome visit (Week 12), all patients received an IV infusion with eptinezumab 100 mg.

Patients were assigned an electronic headache diary, called an eDiary, at the screening visit and were required to complete daily entries from the screening visit to the primary outcome visit (Week 12) or until the withdrawal visit. Patients used the eDiary in their local language to record information regarding any experienced headaches, such as start time, stop time, headache severity, additional symptoms, and acute headache/migraine medication use. The yes/no responses to headache items and the severity rankings (mild, moderate, or severe) helped investigators track any effects of treatment. Migraine was ranked as either moderate or severe. Information collected from the eDiary was used to derive headache/migraine study endpoints.

Patient population

Adults 18–75 years old (inclusive) with migraine onset at 50 years old or younger were eligible for participation if their migraine diagnosis met the criteria established in the International Classification of Headache Disorders, 3 rd edition (ICHD-3) guidelines: a history of migraine onset ≥ 12 months prior to the screening visit, ≥ 8 migraine days per month for the 3 months prior to the screening visit, and a diagnosis of MOH as defined by ICHD-3 guidelines (i.e., the patient had headache on ≥ 15 days/month for the past 3 months prior to the screening visit and had regular overuse of one or more drugs that can be taken for acute and/or symptomatic treatment of headache, for > 3 months) [ 5 ]. The MOH diagnosis was given during an in-person interview at the screening visit by an investigator who received specific training regarding the diagnosis of MOH. Preventive treatment of migraine (prescription or over-the-counter medication recommended by a healthcare professional) was allowed provided the dose and regimen was stable for ≥ 12 weeks prior to the screening visit and expected to be maintained until the end of treatment visit (Week 24).

In this study, a migraine day was defined as any day with a headache that meets the CM definition as outlined in the International Headache Society guidelines (section 1.3.1.1) [ 26 ] for controlled trials of preventive treatment of CM in adults. This includes any day with a headache longer than 4 h in duration, headache meeting ICHD-3 items C and D (migraine without aura), or a headache at least 30 min long plus aura symptoms. A migraine day was also defined based on patient perception of migraine severity; that is, a day with a headache at least 30 min long believed by the patient to be a migraine and for which the patient took a triptan, ergotamine, or other migraine-specific acute medication also met the criteria.

Adults were ineligible for study participation if previous anti-CGPR treatment(s) failed or if they had confounding and clinically significant pain syndromes, an acute or active temporomandibular disorder diagnosis, other headache type diagnosis, clinically significant cardiovascular disease, or an uncontrolled/untreated psychiatric condition for ≥ 6 months prior. Full inclusion and exclusion criteria are detailed in the protocol.

Randomization

Patients were randomly allocated via an interactive response technology system to one of the two treatment groups: eptinezumab 100 mg or placebo, in a 1:1 ratio. Additionally, all patients were to receive eptinezumab 100 mg in the open-label period. Thus, no patient was denied access to active treatment with eptinezumab. The term “treatment sequence” is used to denote the treatment groups arising by combining the treatment received in the placebo-controlled period and the eptinezumab 100 mg received in the open-label period. Therefore, in the open-label period, the two treatment sequence groups were: placebo–eptinezumab 100 mg and eptinezumab 100 mg–eptinezumab 100 mg. The interactive response technology allocated patients to a treatment group and assigned a randomization number that was used to identify the patient throughout the study. Study site and number of MHDs (< 20/ ≥ 20 MHDs at baseline) data collected during the screening period was used to stratify the randomization.

Study outcomes

The primary endpoint for efficacy was the change from baseline in MMDs over the 12-week placebo-controlled period (Weeks 1–12). Key secondary endpoints, listed in the testing hierarchy order, were the change from baseline in MMDs with use of AHM (Weeks 1–12), proportion of patients with ≥ 50% reduction from baseline in MMDs (migraine responder rate [MRR]; Weeks 1–12), migraine rate on the day after dosing (Day 1), proportion of patients with ≥ 75% reduction from baseline in MMDs (Weeks 1–4), change from baseline in the number of MHDs (Weeks 1–12), and proportion of patients with ≥ 75% reduction from baseline in MMDs (Weeks 1–12). Additional prespecified secondary and exploratory endpoints and the safety endpoints are summarized in Supplemental Table 1 .

Patient-reported outcomes

All patient-reported outcomes were administered in the local language and validated in the language to which they were translated. The Patient Global Impression of Change (PGIC) instructs patients to rate their improvement due to treatment and uses a rating system with 7 categories of change (“very much improved”, “much improved”, “minimally improved”, “no change”, “minimally worse”, “much worse”, and “very much worse”). The lower the score, the greater the patient’s perceived improvement in their disease-related functioning [ 27 ].

During the screening visit, investigators verbally asked patients for their patient-identified most bothersome symptom (PI-MBS) related to migraine, which was then categorized by the investigator into one of the following choices: nausea, vomiting, light sensitivity, sound sensitivity, mental cloudiness, fatigue pain with activity, mood changes, and “other/specify” (for alternative answers). Improvements were rated on a 7-point scale similar to that of PGIC, with lower scores indicating greater improvement in the most bothersome symptom [ 28 ]. Additional methods for patient-reported outcomes can be found in the Supplemental Methods section.

Statistical analysis

In a prior study of eptinezumab, the subgroup of CM patients with MOH showed an improvement of 3.0 MMDs for the 100-mg dose compared to placebo, with a standard deviation of 6.0 [ 8 ]. Assuming the same effect size, 86 patients per treatment group provided a power of 90% for the primary endpoint using a 5% significance level. To account for 5% of randomized patients not contributing to the primary endpoint, 91 patients randomized per treatment group—or 182 randomized patients in total—provided a power of 90% to detect an effect size as mentioned for the MOH subgroup.

The estimand for the primary endpoint was described by the following attributes. The first attribute was the population of interest, which was patients with a dual diagnosis of migraine and MOH who fulfilled the inclusion and exclusion criteria of the study. The second attribute was the endpoint to be considered, which was the change from baseline in MMDs (Weeks 1–12). The third attribute was the treatment condition of interest, which was the comparison of eptinezumab 100 mg to placebo, with or without the use of preventive migraine medication. The fourth attribute was the other intercurrent event of interest, which was handled with a treatment policy strategy to assess the effect regardless of infusion interruption or termination before full dose is received. The last attribute was the population level summary, which was the mean difference in the primary endpoint across Weeks 1–12 comparing the effect of eptinezumab 100 mg to placebo.

The main estimator for the primary estimand was based on the primary endpoint, change from baseline in the number of MMDs (Weeks 1–12), which was estimated using a restricted maximum likelihood–based mixed model for repeated measures (MMRM) approach. The analysis was performed on MMDs by month using an MMRM, with month defined as 4-week intervals (Weeks 1–4, Weeks 5–8, Weeks 9–12), with baseline MMDs as a continuous covariate, and treatment, stratum (< 20 MHDs, ≥ 20 MHDs at baseline), month, and region as fixed factors. In addition, the model included treatment-by-month interaction, baseline MMDs-by-month interaction, and stratum-by-month interaction. Within-patient errors were modeled using an unstructured variance.

For the key secondary endpoints based on responder rates, treatment effects compared to placebo were analyzed using a logistic regression model that included MMDs at baseline as a continuous covariate, and treatment and stratification factor (< 20 MHDs, ≥ 20 MHDs at baseline) as factors. Migraine rate on the day after dosing (Day 1), was analyzed using a Cochran–Mantel–Haenszel test controlling for stratification factor (< 20 MHDs, ≥ 20 MHDs at baseline). Change from baseline in the number of MMDs with use of AHM (Weeks 1–12) and change from baseline in the number of MHDs (Weeks 1–12) were analyzed similarly to the primary endpoint.

The formal statistical testing of the primary endpoint and the 6 key secondary endpoints was done hierarchically, in a sequence of a maximum number of 7 steps. For each step, the treatment effect was tested on a 5% significance level, using a two-sided test, and testing only continued to the next step if all prior effects in the hierarchy were found to have p-values below the specified significance level (Supplemental Figure 2 ). For subgroup analyses, the analysis specified for the primary endpoint was repeated by region (Asia and Europe), sex, age group (≤ 35 years and > 35 years), the stratification factor (< 20 MHDs and ≥ 20 MHDs at baseline), and the number of previous preventive treatment failures (0, ≥ 1). Furthermore, a post hoc analysis of the primary and key secondary endpoints was presented separately for Chinese patients (i.e., patients from Mainland China and Taiwan).

Study population

Between February 2021 and February 2022, a total of 332 patients were screened; 193 patients (the all-patients-treated set) with a dual diagnosis of migraine and MOH were randomized to eptinezumab 100 mg ( n  = 93) or placebo ( n  = 100). A total of 164 patients completed the placebo-controlled period (Fig.  1 ), and 29 patients withdrew. Baseline demographics and clinical characteristics of the full analysis set (FAS; n  = 190; eptinezumab 100 mg [ n  = 90] and placebo [ n  = 100]) were generally similar between treatment groups. Three patients from the eptinezumab group were excluded from the FAS because no post-baseline primary endpoint data were contributed. Regarding the two treatment groups, most patients were female (148/190 [77.9%]), with a median age of 43.5 years. There was a slightly higher percentage of males in the eptinezumab group than in the placebo group (24/90 [26.7%] vs 18/100 [18%]; Table 1 ). Patients had on average 19.6 MMDs and 20.8 MHDs at baseline, with an average of 19.1 days per month of AHM use. The pattern of previous preventive treatment failures was similar across treatment groups; 57.4% of enrolled patients did not report previous preventive treatment failures (Supplemental Figure 3 ).

Patient disposition (placebo-controlled period). *Completed and withdrawn data refer to the number of patients completing or withdrawing in the placebo-controlled period. **Three patients from the eptinezumab group were excluded from the full analysis set because no post-baseline primary endpoint data were contributed. AE, adverse event

Efficacy outcomes

At baseline, mean MMDs were similar across treatment groups (eptinezumab, 19.5; placebo, 19.7). Over Weeks 1–12, eptinezumab reduced mean MMDs more than placebo (difference from placebo [95% confidence interval] between treatments was -1.2 [-2.9 to 0.4]; p  = 0.1484); i.e., this finding was not statistically significant (primary endpoint, Fig.  2 a, Table 2 ). The reduction in MMDs over Weeks 1–4 showed greater reductions with eptinezumab (7.1 MMDs) than with placebo (5.1 MMDs; p  = 0.0191 vs placebo; Fig.  2 b). At baseline, the number of mean MMDs with AHM use was similar across treatment groups (eptinezumab, 18.9; placebo, 19.2). Changes from baseline in MMDs with AHM use over Weeks 1–12 followed a similar pattern, where eptinezumab reduced mean MMDs with AHM more than placebo (difference between treatments was -1.3; p  = 0.1363; key secondary endpoint, Supplemental Figure 4 a, Table 2 ). The reduction in MMDs with AHM use for patients treated with eptinezumab was greater over Weeks 1–4, with a reduction of 7.4 MMDs and 5.4 MMDs in the eptinezumab and placebo groups, respectively ( p  = 0.0196; Supplemental Figure 4 b).

Change from baseline in mean MMDs ( A ) Weeks 1–12 and ( B ) 4-week intervals (FAS). The estimated means, mean differences from placebo, and 95% confidence intervals are from a mixed model for repeated measures with month (Weeks 1–4, Weeks 5–8, Weeks 9–12), region, stratification factor (monthly headache days at baseline: < 20/ ≥ 20), and treatment as factors, baseline score as a continuous covariate, treatment-by-month interaction, baseline score-by-month interaction, and stratum-by-month interaction. Data represent mean ± standard error. FAS, full analysis set; MMDs, monthly migraine days

The eptinezumab group showed a numerically higher proportion of patients than the placebo group with ≥ 50% reductions from baseline in MMDs (31.1% compared to 24.0%, respectively; p  = 0.2563; key secondary endpoint; Fig.  3 , Table 2 ). Moreover, patients treated with eptinezumab during Weeks 1–12 were more likely than those treated with placebo to achieve, relative to baseline, a ≥ 75% in MMDs (16.7% compared to 2%, respectively; p  = 0.0002; key secondary endpoint; Fig.  3 , Table 2 ). A smaller percentage of patients treated with eptinezumab had migraine on the day after dosing compared to the placebo group (eptinezumab, 44.2%; placebo, 59.2%; p  = 0.0315; Supplemental Figure 5 , Table 2 ).

Patients with ≥ 50% and ≥ 75% reduction from baseline in MMDs over Weeks 1–12 (FAS). The 50% and 75% response variables across the three 4-week intervals are calculated as the average percentage change in MMDs (based on the available monthly values of MMDs). The comparison is based on logistic regression model including baseline MMDs as a continuous covariate, and treatment and stratification factor (monthly headache days at baseline: < 20/ ≥ 20) as factors. If the MMD value is missing for a given month, the responder status is derived based on the available values. n indicates the number of patients with observations. Data represent mean percentages. FAS, full analysis set; MMD, monthly migraine days; MRR, migraine responder rate

Larger improvements were observed in both PGIC and PI-MBS scores at Week 12 in the eptinezumab-treated group, with the mean PGIC scores being 2.6 for eptinezumab and 3.1 for placebo ( p  = 0.0037; Fig.  4 ) and the mean PI-MBS scores being 2.7 for eptinezumab and 3.2 for placebo ( p  = 0.0074). There was a higher proportion of patients achieving clinical significance (a 5-point reduction) in the 6-item Headache Impact Test (HIT-6) total score at Week 12 in the eptinezumab-treated group (57.6 eptinezumab vs 46.8 placebo; p  = 0.0516; Supplemental Figure 6 a) [ 29 ].

Patient Global Impression of Change ( A ) and patient-identified most bothersome symptom ( B ) scores (FAS). The model includes the following fixed effects: visit, region, stratification factor (monthly headache days at baseline: < 20/ ≥ 20), and treatment as factors, treatment-by-visit interaction, and stratum-by-visit interaction. The PGIC and the PI-MBS are ranked on a scale of 1–7, and the lower the score the higher the clinical improvement. Patients could rate their change on the PGIC and PI-MBS scale as “Very much improved”, “much improved”, “minimally improved”, “no change”, “minimally worse”, “much worse”, or “very much worse”. Data represent mean ± standard error. FAS, full analysis set; PGIC, Patient Global Impression of Change; PI-MBS, patient-identified most bothersome symptom

When analyzing the change from baseline in Migraine-Specific Quality of Life Questionnaire subscores at Week 12 between treatments, role function for both restrictive ( p  = 0.0445) and preventive ( p  = 0.0434) categories favored eptinezumab treatment (Supplemental Figure 6 b). Overall improvement in EQ-5D-5L Visual Analog Scale (VAS) scores was greater in the eptinezumab-treated group over Weeks 1–12 (Supplemental Figure 7 ). Similarly, change from baseline in migraine Work Productivity and Activity Impairment (WPAI:M) subscores (absenteeism, presenteeism, work productivity loss, and activity impairment) were generally numerically in favor of the eptinezumab-treated patients (Supplemental Figure 8 a-d) [ 30 ].

Subgroup analyses: efficacy outcomes

A post hoc analysis in Chinese patients ( n  = 137), changes from baseline in MMDs over Weeks 1–12 followed a similar pattern as observed in the FAS, where eptinezumab reduced mean MMDs by 7.1 compared to 5.6 in the placebo group ( p  = 0.1584; Table 3 ). In the smaller subgroup of European patients ( n  = 35) in this study, the change from baseline in MMDs over Weeks 1–12 was more pronounced in the eptinezumab-treated group compared to the placebo group (8.6 mean MMD reduction compared to 5.4 in the placebo group [ p  = 0.1756; Table 4 ]), whereas in the complementary group (Asian patients, n  = 155) the mean MMD reduction was 6.5 for the eptinezumab-treated group compared to 5.6 in the placebo group ( p  = 0.3280; Supplemental Table 2 ).

In women treated with eptinezumab ( n  = 148), a numerically greater difference from placebo in change from baseline in MMDs was observed than what was observed in men ( n  = 42; -1.6 [ p  = 0.0964] compared to -0.1 [ p  = 0.9662], respectively; Supplemental Table 2 ). Moreover, in patients with fewer previous preventive treatment failures, a post hoc analysis showed there was a numerically greater change from baseline for the eptinezumab-treated group when compared to placebo, with a difference of -1.6 MMDs for 0 previous preventive treatment failures ( p  = 0.1672) and -0.4 MMDs for ≥ 1 previous preventive treatment failure ( p  = 0.7424; Supplemental Table 2 ).

Safety and tolerability

During the placebo-controlled period, vital signs, laboratory values, and electrocardiograms (ECGs) did not show any clinically relevant safety findings. Thirty-four percent of patients in the placebo group and 41% of patients in the eptinezumab group experienced treatment-emergent adverse events (TEAEs; Table 5 ). No TEAEs led to infusion interruption or termination. One TEAE in the placebo group and 2 in the eptinezumab group led to patient withdrawal from the study. Two serious adverse events were reported in the eptinezumab treatment group (1 acute myocardial infarction [the patient was withdrawn from the study and fully recovered] and 1 rib fracture).

During the open-label period, vital signs, laboratory values, and ECGs did not reveal any clinically relevant safety findings. Forty-seven percent of patients randomized to the placebo − eptinezumab 100-mg treatment sequence group and 42% of patients randomized to the eptinezumab 100-mg − eptinezumab 100-mg treatment sequence group experienced TEAEs (Table 6 ). Similar to the placebo-controlled period, no TEAEs led to infusion interruption or termination. One TEAE in the placebo − eptinezumab 100-mg treatment sequence group and 1 in the eptinezumab 100-mg − eptinezumab 100-mg treatment sequence group led to patient withdrawal from the study. Seven serious adverse events were reported by 4 patients in the eptinezumab 100-mg − eptinezumab 100-mg treatment sequence group (preferred terms: bronchitis, headache, dermal cyst, intervertebral disc protrusion, radiculopathy, and pharyngitis).

The objective of this study was to evaluate the efficacy and safety of eptinezumab to prevent migraine and headache in a predominantly Asian patient population with a dual diagnosis of migraine and MOH. Although the study’s primary endpoint did not meet statistical significance and the hierarchical testing strategy was stopped after the first primary endpoint hypothesis test, the data consistently trended in favor of eptinezumab treatment versus placebo, with p-values below 0.05 observed in 3 out of the 6 key secondary endpoints (migraine on the day after dosing, ≥ 75% reduction from baseline in MMDs [Weeks 1–4], ≥ 75% reduction from baseline in MMDs [Weeks 1–12], as well as in the PGIC and PI-MBS patient perception of change).

There can be many reasons why the primary endpoint of the study was not met. One reason is a smaller effect size than anticipated, and another reason is that the sample size was smaller than previous studies of eptinezumab in migraine prevention studies [ 19 , 21 , 31 ]. Moreover, there might be both extrinsic and intrinsic ethnic factors involved, causing this trial population to be potentially different when compared to the global migraine population from previous trials. This could be because of cultural differences in healthcare and migraine management, including clinical research (such as differences in clinical trial recruitment between different countries or how patients report headache/migraine characteristics). Differences observed in this study may show that these two patient populations (European and Asian) may be interpreting headache and migraine definitions differently (e.g., considering non-migraine headaches to be migraine), leading to different results. Similarly, there may be a difference in patient reporting between the primary endpoint and secondary endpoints. All these aspects constitute examples of potential extrinsic ethnic factors that might play a role here. The fact that the headache diary does not tell the same story as the patient-reported outcomes may in fact show that the different cultures interact differently with this eDiary and with the clinical practice in clinical trials. The estimated change in MMDs for European patients were similar to the effect seen in previous eptinezumab trials [ 19 , 21 , 31 ]. This study contained a higher percentage of men; therefore, sex might constitute an intrinsic factor, making the study potentially less comparable to the general migraine population from previous trials.

Patients with MOH were included in this study and there are differences between Asian and European patients, including the overused medications and clinical manifestations of MOH, which can also partly explain the results [ 32 ]. To meet MOH diagnostic criteria according to ICHD-3 criteria, patients must have a primary headache disorder with headache on ≥ 15 days per month in conjunction with overuse of acute treatments (defined as ≥ 10 or ≥ 15 days per month depending on the medication class) [ 5 ]. At baseline, mean MMDs with use of AHM (19.1 days) in the SUNLIGHT population suggests that these participants had had room for improvement in both MMD and AHM use which may have contributed to a larger than expected placebo effect. In the placebo group and the eptinezumab group, improvements in changes from baseline in MMDs with AHM over Weeks 1–12 were observed (-6.2 and -7.5, respectively). In patients with CM and MOH, reduction of AHM use may be an effective strategy to decrease MMDs and headache severity [ 32 ]. Similar observations have been observed in other studies with participants with MOH [ 33 , 34 ]. Despite the present study not including patient education or behavioural interventions in a standardized way, patients may have spontaneously reduced their use of AHM knowing of their inclusion in an MOH-study. This may have been a contributing factor to improvement in both treatment groups and the lack of significance observed for the primary endpoint. Other aspects that may have contributed include the fact that AHM use/reduction was not controlled or that combination analgesics with unknown ingredients may have difficult-to-predict efficacy and washout times.

Of note, in the SUNLIGHT patient population, 57% of the enrolled patients had no previous preventive treatment failures. In the previous subgroup analysis of patients with CM and MOH in a larger study from the US and Europe, all patients treated with eptinezumab reported prior use of an oral preventive [ 22 ]. This lack of previous preventive treatment failures for headache/migraine may have led to a higher placebo response rate as well as the cultural factors previously discussed [ 21 ]. Importantly, like previously published studies [ 19 , 21 , 31 ], eptinezumab was well tolerated in the patient population studied here. In both the placebo-controlled and open-label periods, no new safety signals were identified.

Limitations

This was a comparatively small study population in which the statistical power was set using a large, assumed difference in the reduction of MMDs from baseline to Weeks 1–12 (sample size assumptions were based on the MOH subgroup from PROMISE-2) [ 31 ]. Moreover, patients with previous anti-CGRP therapy failures, as well as clinically significant cardiovascular disease or confounding pain syndromes, were excluded from participation; therefore, the findings may not be indicative of safety and efficacy in patients with these or other excluded conditions.

Conclusions

This study did not meet its primary efficacy endpoint; it is therefore negative. Overall, however, all efficacy endpoints numerically favored eptinezumab treatment when compared to placebo. In addition, the SUNLIGHT study had similar safety and tolerability compared to previous trials in the overall study population and in the Asian patients, who represented the majority of the population. Moreover, the patient-reported outcome results were aligned with expectations of eptinezumab treatment increasing quality of life. Like previous studies, eptinezumab was proven to be well tolerated in both the placebo-controlled and the open-label period of this study.

Availability of data and materials

In accordance with The European Federation of Pharmaceutical Industries and Associations (EFPIA) and the Pharmaceutical Research and Manufacturers of America (PhRMA) Principles for Responsible Clinical Trial Data Sharing guidelines, H. Lundbeck is committed to responsible sharing of clinical trial data in a manner that is consistent with safeguarding the privacy of patients, respecting the integrity of national regulatory systems, and protecting the intellectual property of the sponsor. The protection of intellectual property ensures continued research and innovation in the pharmaceutical industry. Deidentified data are available to those whose request has been reviewed and approved through an application submitted to https://www.lundbeck.com/global/our-science/clinical-data-sharing .

Abbreviations

Acute headache medication

All-patients-treated set

All-patients-treated-open-label set

Calcitonin gene-related peptide

• Chronic migraine

Electrocardiogram

International Classification of Headache Disorders, 3rd edition

Intravenous

Full analysis set

6-Item Headache Impact Test

• Medication-overuse headache

Monthly migraine days

Monthly headache days

Mixed model for repeated measures

Migraine responder rate

Migraine-Specific Quality of Life Questionnaire

Patient Global Impression of Change

Patient-identified most bothersome symptom

Treatment-emergent adverse events

Work Productivity and Activity Impairment.

Visual Analog Scale

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Acknowledgements

The authors thank the participants of this study. The authors also thank Julia L. Jones, PhD and Nicole Coolbaugh, CMPP of The Medicine Group, LLC (New Hope, PA, United States) for providing medical writing support, which was funded by H. Lundbeck A/S (Copenhagen, Denmark) and in accordance with Good Publication Practice guidelines.

The study and medical writing support for the development of the manuscript was sponsored and funded by H. Lundbeck A/S (Copenhagen, Denmark).

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Shengyuan Yu

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Neurology Department, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China

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H. Lundbeck A/S, Copenhagen, Denmark

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Neurology Department, Headache Unit, Vall d’Hebron University Hospital, Barcelona, Spain

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Contributions

All authors had full access to the study data and take responsibility for the integrity of the data and the accuracy of the data analysis. SY and IF contributed to the conception or design of the study. SY, JZ, GL, ZX, and PPR were study investigators, who contributed to the acquisition of data. ZX, GJ, and KR directly accessed and verified the underlying data. GJ and KR conducted the statistical analyses, and all authors contributed to the interpretation of data. SY, JZ, GL, ZX, AE, GJ, IF, KR, and PPR were involved in drafting the manuscript and critically revised the manuscript for important intellectual content. All authors provided final approval of the manuscript content for submission and had final responsibility for the decision to submit for publication.

Corresponding author

Correspondence to Shengyuan Yu .

Ethics declarations

Ethics approval and consent to participate.

The study was conducted in accordance with standards of Good Clinical Practice as defined by the International Conference on Harmonisation and all applicable federal and local regulations. All study documentation was approved by the local review board at each site or by a central institutional review board/ethics committee; there were a total of 40 sites: The First Affiliated Hospital of Guangzhou Medical University (Guangzhou, Guangdong, China, 510120), Beijing Chaoyang Hospital Capital Medical University (Beijing, China, 100020), Beijing Anzhen Hospital, Capital Medical University (Beijing, China, 100029), Peking University First Hospital (Beijing, China, 100034), Xuanwu Hospital Capital Medical University (Beijing, China, 100053), Peking Union Medical College Hospital (Beijing, China, 100730), Chinese PLA General Hospital (Beijing, China, 100853), Jiangsu Province Hospital (the First Affiliated Hospital With Nanjing Medical University) (Beijing, China, 610041), The First Hospital of Jilin University (Changchun, China, 130021), The Second Hospital of Jilin University (Changchun, China, 130022), The First Affiliated Hospital of Chongqing Medical University (Chongqing, China, 400016), The Affiliated Hospital of Guizhou Medical University (Guiyang, China, 550000), Mianyang Central Hospital (Mianyang, China, 621000), Jiangxi Pingxiang People's Hospital (Pingxiang, China, 337055), People's Hospital of Rizhao (Rizhao, China, 276826), Shengjing Hospital of China Medical University (Shenyang, China, 110004), General Hospital of Northern Theater Command (Shenyang, China, 110015), The University of Hong Kong—Shenzhen Hospital (Shenzhen, China, 518053), Shanxi Provincial People Hospital (Taiyuan, China, 030012), The 2nd Affiliated Hospital of Wenzhou Medical University (Wenzhou, China, 325035), Union Hospital Tongji Medical College Huazhong University of Science and Technology (Wuhan, China, 430022), Renmin Hospital of Wuhan University (Wuhan, China, 430060), The First Affiliated Hospital of Xi'an Jiaotong University (Xi'an, China, 710061), People's Hospital of Zhengzhou (Zhengzhou, China, 450003), The First Affiliated Hospital of Zhengzhou University (Zhengzhou, China, 450052), Affiliated Hospital of Jiangsu University (Zhenjiang, China, 212001), Pineo Medical Ecosystem (Tbilisi, Georgia, 0114), Aversi Clinic LTD (Tbilisi, Georgia, 0160), Nowon Eulji Medical Center, Eulji University (Seoul, Korea, Republic of, 01830), Severance Hospital Yonsei University Health System—PPDS (Seoul, Korea, Republic of, 03722), Samsung Medical Center—PPDS (Seoul, Korea, Republic of, 06351), Hospital Universitario Puerta de Hierro—Majadahonda (Majadahonda, Madrid, Spain, 28222), Hospital Universitario Vall d'Hebron—PPDS (Barcelona, Spain, 8035), Hospital Universitario La Paz—PPDS (Madrid, Spain, 28046), Hospital Clinico Universitario de Valencia (Valencia, Spain, 46010), Hospital Universitari i Politecnic La Fe de Valencia (Valencia, Spain, 46026), Hospital Clinico Universitario Lozano Blesa (Zaragoza, Spain, 50009), Taipei Veterans General Hospital (Taipei City, Taiwan, 11217), Tri-Service General Hospital (Taipei, Taiwan, 11490), Chang Gung Memorial Hospital, Linkou (Taoyuan City, Taiwan, 33305). All patients provided written informed consent prior to their participation in the study.

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Not applicable.

Competing interests

SY, JZ, GL, and ZX disclose no conflicts of interest. AE, GJ, KR, and IF are full-time employees of H. Lundbeck A/S. PPR reports honoraria as a consultant and participation in the last 3 years in advisory boards for AbbVie, Amgen, Biohaven, Chiesi, Eli Lilly, Lundbeck, Novartis, Pfizer, and Teva Pharmaceuticals; institutional research support from AbbVie, AGAUR, ERA-NET NEURON, Instituto Investigación Carlos III, International Headache Society, Novartis, PERIS, RIS3CAT FEDER, and Teva Pharmaceuticals; being a principle investigator for more than 45 clinical trials (phase II, III, and IV) for the preventive treatment of migraine and other headaches; education projects with AbbVie, Almirall, Chiesi, Eli Lilly, Lundbeck, Medlink, Medscape, Neurodiem, Novartis, and Teva Pharmaceuticals; participation in the Scientific Advisory Board of Lilly Foundation Spain and Honorary Secretary of the International Headache Society; and being an associate editor for Cephalalgia, Headache, Neurologia, Frontiers of Neurology , director for headache section of Revista de Neurologia , and editorial advisor for Journal of Headache and Pain .

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Supplementary Information

Additional file 1:.

Supplemental Table 1. Study objectives and endpoints. Supplemental Table 2. Analysis of change from baseline in MMDs (Weeks 1–12) across various subgroups (FAS). Supplemental Figure 1. SUNLIGHT study design. Supplemental Figure 2. Statistical testing hierarchy for primary and key secondary endpoints. Supplemental Figure 3. Number of previous preventive treatment failures (FAS). Supplemental Figure 4. Change from baseline in MMDs with AHM use over (A) Weeks 1–12 and (B) 4-week intervals (FAS). Supplemental Figure 5. Percentage of patients with migraine on the day after the first dose (FAS). Supplemental Figure 6. Analysis of (A) proportion of patients achieving a 5-point reduction in HIT-6 total score at Week 12 and (B) change from baseline in MSQ subscores at Week 12 (FAS). Supplemental Figure 7. Mean change from baseline in EQ-5D-5L VAS score (FAS). Supplemental Figure 8. Change from baseline in WPAI:M subscores: (A) absenteeism, (B) presenteeism, (C) work productivity loss, and (D) activity impairment (FAS).

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Yu, S., Zhou, J., Luo, G. et al. Efficacy and safety of eptinezumab in patients with chronic migraine and medication-overuse headache: a randomized, double-blind, placebo-controlled study. BMC Neurol 23 , 441 (2023). https://doi.org/10.1186/s12883-023-03477-z

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DOI : https://doi.org/10.1186/s12883-023-03477-z

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A randomized, double-blind, positive-controlled, 3-way cross-over human experimental pain study of a TRPV1 antagonist (V116517) in healthy volunteers and comparison with preclinical profile

Affiliations.

• 1 SMI, Department of Health Science and Technology, Faculty of Medicine, Aalborg University, Aalborg, Denmark.
• 2 C4Pain, Aalborg, Denmark.
• 3 Purdue Pharma, Stamford, CT, USA.
• PMID: 27168361
• DOI: 10.1097/j.pain.0000000000000610

This experimental, translational, experimental pain, single-center, randomized, double-blind, single-dose, 3-treatment, 3-period cross-over proof-of-concept volunteer trial studied the efficacy of a novel TRPV1 antagonist (V116517) on capsaicin- and UV-B-induced hyperalgesia. Heat and pressure pain thresholds, von Frey stimulus-response functions, and neurogenic inflammation were assessed together with safety. Each treatment period was 4 days. The 3 single oral treatments were 300 mg V116517, 400 mg celecoxib (a COX-2 inhibitor), and placebo. The heat pain detection and tolerance thresholds were increased significantly (P < 0.0001) by V116517. Heat pain detection and tolerance thresholds showed significantly less capsaicin hyperalgesia after V116517 (P = 0.004 and P < 0.0001, respectively). Celecoxib reduced UV-B-provoked pressure pain sensitization (P = 0.01). Laser Doppler flowmetry and erythema index after UV-B were significantly (P < 0.0001) reduced by celecoxib. Stimulus-response function in capsaicin-treated areas showed significant differences between both celecoxib and placebo and between V116517 and placebo. The body temperature showed no change, and no side effects were reported for any of the treatments. The TRPV1 antagonists and the COX-2 inhibitor showed different antihyperalgesic profiles indicating different clinical targets. In addition, the preclinical profile of V116517 in rat models of UV-B and capsaicin-induced hypersensitivity was compared with the human experimental data and overall demonstrated an alignment between 2 of the 3 end points tested. The TRPV1 antagonist showed a potent antihyperalgesic action without changing the body temperature but heat analgesia may be a potential safety issue.

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A Randomized, Parallel-Arm, Double-Blind, Placebo-Controlled Study With Open-Label Extension to Assess the Efficacy and Safety of Vatiquinone for the Treatment of Friedreich Ataxia (MOVE-FA)

Study overview:.

The primary objective of the study is to evaluate the efficacy (using the modified Friedreich Ataxia Rating Scale [mFARS]) and safety of vatiquinone in participants with Friedreich ataxia (FA).

Anyone considering participating in a clinical trial should discuss the matter with their physician. FARA does not endorse or recommend any particular studies.

Study Details:

During the double-blind, placebo-controlled phase, participants will be stratified by baseline mFARS score (<40 versus ≥40), age of disease onset (<14 versus ≥14), and age at screening (≤21 years or >21 years) and randomized to receive either vatiquinone or placebo using interactive response technology (IRT). Following completion of the randomized, double-blind, placebo-controlled phase (72 weeks), participants will enter into an open-label extension phase (24 weeks) during which they will receive open-label treatment with vatiquinone at the dose they received in the randomized phase of the study (for participants entering the extension phase who initially received placebo, the dose of vatiquinone will be determined based on age and weight) and then a safety follow-up (approximately 30 days [±5 days] after last dose or termination visit, whichever is later).

The primary efficacy analysis will be based on change from baseline in mFARS score of participants between 7 and 21 years old. In order to explore the treatment efficacy and safety, approximately an additional 20 participants >21 years of age will be randomized for a total of approximately 126 participants.

Key Inclusion Criteria:

• mFARS ≥20 to ≤70 at baseline
• Must be able to ambulate at least 10 feet in 1 minute with or without assistance (non-wheelchair).
• Friedreich ataxia diagnosis (homozygous for guanine-adenine-adenine [GAA] repeat expansion in intron-1 of frataxin [FXN] gene), confirmed by clinical testing (Note: size of GAA repeat is not required for eligibility)
• Consent to comply with study procedures. For participants under the age of 18 (or age of consent), parent(s)/legal guardian(s) of the participant must agree to comply with the requirements of the study, including the need for frequent and prolonged follow up; parent(s)/legal guardian(s) with custody of the participant must give their consent for participant to enroll in the study.
• Difference in the mFARS at screening and baseline of no more than 4 points.
• Must be able to abstain from anticoagulants and any aspirin (including 81 mg) for 30 days prior to the baseline visit and for the duration of the study; any possible discontinuation of anticoagulants should be monitored and indicated by a specialist (for example, cardiologist, neurologist, or hematologist) and discontinuation will be noted by the prescribing physician.
• Must be able to abstain from potent cytochrome P450 (CYP) 3A4 inducers/inhibitors (for example, ketoconazole, rifampin, St. John’s wort, grapefruit juice or any grapefruit product) for at least 30 days prior to enrollment
• Must be able to swallow capsules
• Males and females of childbearing potential must be willing to use an effective method of contraception from the time consent is signed until 30 days after the last dose of study drug or early termination visit. Male participants must agree not to donate sperm during the study and for at least 30 days after the last dose of study drug or early termination visit.

Key Exclusion Criteria:

• Individuals with clinical diagnosis of FA who have point mutations or deletions or other non-GAA expansion mutations
• Previous treatment with vatiquinone
• Allergy to vatiquinone, sesame oil, gelatin (bovine and/or porcine), titanium dioxide, or red iron oxide
• Ejection fraction <50%
• Uncontrolled diabetes (glycated hemoglobin [HbA1c] >7.0%) at the time of screening
• Has current suicidal ideation based on Columbia-Suicide Severity Rating Scale (C-SSRS) within 3 months prior to screening or between screening and baseline at the baseline visit or suicidal behavior within the last year at the screening visit or between screening and baseline at the baseline visit
• Pregnant or lactating participants or those sexually active participants who are unwilling to comply with proper birth control methods; females of childbearing potential must have a negative pregnancy test at screening and during the baseline visit
• Aspartate aminotransferase (AST) or alanine aminotransferase (ALT) ≥2 * upper limit of normal (ULN) at time of screening
• International normalized ratio (INR) ≥1.5 * ULN at time of screening or clinically significant (CS) bleeding, as determined by the investigator
• Serum creatinine ≥1.5 * ULN at time of screening
• Comorbidities that may confound study results (for example, fat malabsorption syndrome, other mitochondrial disorder) in the opinion of the investigator
• Participation in any other interventional clinical trial or received any investigational drug in any other clinical trial within 60 days prior to the baseline visit. Participants may be rescreened after the exclusionary period of 60 days has passed.
• Concomitant use of interventional coenzyme Q10 (CoQ10), vitamin E, or any approved or non-approved medication for FA within 30 days prior to the screening visit. These prohibited medications can be discontinued at the screening visit; if this is the case, the mFARS assessment must be repeated to confirm inclusion eligibility after a minimum of 30 days post-discontinuation and there must be no more than a 4-point difference in mFARS assessed from the post-discontinuation visit to the baseline visit.
• Illicit drug use 30 days prior to screening and during the study is prohibited.

Additional inclusion and exclusion criteria may apply and will be evaluated by a study doctor.  

Length of Study Commitment:

72 weeks (Initial Study), plus 24 weeks (Open Label Extension)

Participating Study Locations

More Information

Explore the fa drug development pipeline.

FARA believes that there are many different approaches to treating Friedreich’s ataxia, and that it will require a cocktail approach of two or more treatments to slow, stop, reverse, and cure FA. Learn more about the approach behind this potential treatment and explore the other approaches that are in the FA Drug Development pipeline.

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Randomised controlled trials—the gold standard for effectiveness research

Eduardo hariton.

1 Department of Obstetrics, Gynecology, and Reproductive Biology, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA, 02116, USA

Joseph J. Locascio

2 Department of Neurology, Massachusetts General Hospital, 15 Parkman Street, Boston, Massachusetts 02114

Randomized controlled trials (RCT) are prospective studies that measure the effectiveness of a new intervention or treatment. Although no study is likely on its own to prove causality, randomization reduces bias and provides a rigorous tool to examine cause-effect relationships between an intervention and outcome. This is because the act of randomization balances participant characteristics (both observed and unobserved) between the groups allowing attribution of any differences in outcome to the study intervention. This is not possible with any other study design.

In designing an RCT, researchers must carefully select the population, the interventions to be compared and the outcomes of interest. Once these are defined, the number of participants needed to reliably determine if such a relationship exists is calculated (power calculation). Participants are then recruited and randomly assigned to either the intervention or the comparator group. 1 It is important to ensure that at the time of recruitment there is no knowledge of which group the participant will be allocated to; this is known as concealment. This is often ensured by using automated randomization systems (e.g. computer generated). RCTs are often blinded so that participants and doctors, nurses or researchers do not know what treatment each participant is receiving, further minimizing bias.

RCTs can be analyzed by intentionto-treat analysis (ITT; subjects analyzed in the groups to which they were randomized), per protocol (only participants who completed the treatment originally allocated are analyzed), or other variations, with ITT often regarded least biased. All RCTs should have pre-specified primary outcomes, should be registered with a clinical trials database and should have appropriate ethical approvals.

RCTs can have their drawbacks, including their high cost in terms of time and money, problems with generalisabilty (participants that volunteer to participate might not be representative of the population being studied) and loss to follow up.

USEFUL RESOURCES

• CONSORT Statement: CONsolidated Standards of Reporting Trials guidelines designed to improve the reporting of parallel-group randomized controlled trials - http://www.consort-statement.org/consort-2010
• Link to A Randomized, Controlled Trial of Magnesium Sulfate for the Prevention of Cerebral Palsyin the New England Journal of Medicine – A well designed RCT that had a significant impact in practice patterns. http://www.nejm.org/doi/full/10.1056/NEJMoa0801187#t=abstract

LEARNING POINTS

While expensive and time consuming, RCTs are the gold-standard for studying causal relationships as randomization eliminates much of the bias inherent with other study designs.

To provide true assessment of causality RCTs need to be conducted appropriately (i.e. having concealment of allocation, ITT analysis and blinding when appropriate)

Disclosures: The authors have no financial interests to disclose