statement where the hypothesis and conclusion are switched

Conditional Statement – Definition, Truth Table, Examples, FAQs

What is a conditional statement, how to write a conditional statement, what is a biconditional statement, solved examples on conditional statements, practice problems on conditional statements, frequently asked questions about conditional statements.

A conditional statement is a statement that is written in the “If p, then q” format. Here, the statement p is called the hypothesis and q is called the conclusion. It is a fundamental concept in logic and mathematics. 

Conditional statement symbol :  p → q

A conditional statement consists of two parts.

• The “if” clause, which presents a condition or hypothesis.
• The “then” clause, which indicates the consequence or result that follows if the condition is true. 

Example : If you brush your teeth, then you won’t get cavities.

Hypothesis (Condition): If you brush your teeth

Conclusion (Consequence): then you won’t get cavities 

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Conditional Statement: Definition

A conditional statement is characterized by the presence of “if” as an antecedent and “then” as a consequent. A conditional statement, also known as an “if-then” statement consists of two parts:

• The “if” clause (hypothesis): This part presents a condition, situation, or assertion. It is the initial condition that is being considered.
• The “then” clause (conclusion): This part indicates the consequence, result, or action that will occur if the condition presented in the “if” clause is true or satisfied. 

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Representation of Conditional Statement

The conditional statement of the form ‘If p, then q” is represented as p → q. 

It is pronounced as “p implies q.”

Different ways to express a conditional statement are:

• p implies q
• p is sufficient for q
• q is necessary for p

Parts of a Conditional Statement

There are two parts of conditional statements, hypothesis and conclusion. The hypothesis or condition will begin with the “if” part, and the conclusion or action will begin with the “then” part. A conditional statement is also called “implication.”

Conditional Statements Examples:

Example 1: If it is Sunday, then you can go to play. 

Hypothesis: If it is Sunday

Conclusion: then you can go to play. 

Example 2: If you eat all vegetables, then you can have the dessert.

Condition: If you eat all vegetables

Conclusion: then you can have the dessert 

To form a conditional statement, follow these concise steps:

Step 1 : Identify the condition (antecedent or “if” part) and the consequence (consequent or “then” part) of the statement.

Step 2 : Use the “if… then…” structure to connect the condition and consequence.

Step 3 : Ensure the statement expresses a logical relationship where the condition leads to the consequence.

Example 1 : “If you study (condition), then you will pass the exam (consequence).” 

This conditional statement asserts that studying leads to passing the exam. If you study (condition is true), then you will pass the exam (consequence is also true).

Example 2 : If you arrange the numbers from smallest to largest, then you will have an ascending order.

Hypothesis: If you arrange the numbers from smallest to largest

Conclusion: then you will have an ascending order

Truth Table for Conditional Statement

The truth table for a conditional statement is a table used in logic to explore the relationship between the truth values of two statements. It lists all possible combinations of truth values for “p” and “q” and determines whether the conditional statement is true or false for each combination. 

The truth value of p → q is false only when p is true and q is False. 

If the condition is false, the consequence doesn’t affect the truth of the conditional; it’s always true.

In all the other cases, it is true.

The truth table is helpful in the analysis of possible combinations of truth values for hypothesis or condition and conclusion or action. It is useful to understand the presence of truth or false statements. 

Converse, Inverse, and Contrapositive

The converse, inverse, and contrapositive are three related conditional statements that are derived from an original conditional statement “p → q.” 

Consider a conditional statement: If I run, then I feel great.

• Converse: 

The converse of “p → q” is “q → p.” It reverses the order of the original statement. While the original statement says “if p, then q,” the converse says “if q, then p.” 

Converse: If I feel great, then I run.

• Inverse: 

The inverse of “p → q” is “~p → ~q,” where “” denotes negation (opposite). It negates both the antecedent (p) and the consequent (q). So, if the original statement says “if p, then q,” the inverse says “if not p, then not q.”

Inverse : If I don’t run, then I don’t feel great.

• Contrapositive: 

The contrapositive of “p → q” is “~q → ~p.” It reverses the order and also negates both the statements. So, if the original statement says “if p, then q,” the contrapositive says “if not q, then not p.”

Contrapositive: If I don’t feel great, then I don’t run.

A biconditional statement is a type of compound statement in logic that expresses a bidirectional or two-way relationship between two statements. It asserts that “p” is true if and only if “q” is true, and vice versa. In symbolic notation, a biconditional statement is represented as “p ⟺ q.”

In simpler terms, a biconditional statement means that the truth of “p” and “q” are interdependent. 

If “p” is true, then “q” must also be true, and if “q” is true, then “p” must be true. Conversely, if “p” is false, then “q” must be false, and if “q” is false, then “p” must be false. 

Biconditional statements are often used to express equality, equivalence, or conditions where two statements are mutually dependent for their truth values. 

Examples : 

• I will stop my bike if and only if the traffic light is red.  
• I will stay if and only if you play my favorite song.

Facts about Conditional Statements

• The negation of a conditional statement “p → q” is expressed as “p and not q.” It is denoted as “𝑝 ∧ ∼𝑞.” 
• The conditional statement is not logically equivalent to its converse and inverse.
• The conditional statement is logically equivalent to its contrapositive. 
• Thus, we can write p → q ∼q → ∼p

In this article, we learned about the fundamentals of conditional statements in mathematical logic, including their structure, parts, truth tables, conditional logic examples, and various related concepts. Understanding conditional statements is key to logical reasoning and problem-solving. Now, let’s solve a few examples and practice MCQs for better comprehension.

Example 1: Identify the hypothesis and conclusion. 

If you sing, then I will dance.

Solution : 

Given statement: If you sing, then I will dance.

Here, the antecedent or the hypothesis is “if you sing.”

The conclusion is “then I will dance.”

Example 2: State the converse of the statement: “If the switch is off, then the machine won’t work.” 

Here, p: The switch is off

q: The machine won’t work.

The conditional statement can be denoted as p → q.

Converse of p → q is written by reversing the order of p and q in the original statement.

Converse of  p → q is q → p.

Converse of  p → q: q → p: If the machine won’t work, then the switch is off.

Example 3: What is the truth value of the given conditional statement? 

If 2+2=5 , then pigs can fly.

Solution:  

q: Pigs can fly.

The statement p is false. Now regardless of the truth value of statement q, the overall statement will be true. 

F → F = T

Hence, the truth value of the statement is true. 

Conditional Statement - Definition, Truth Table, Examples, FAQs

Attend this quiz & Test your knowledge.

What is the antecedent in the given conditional statement? If it’s sunny, then I’ll go to the beach.

A conditional statement can be expressed as, what is the converse of “a → b”, when the antecedent is true and the consequent is false, the conditional statement is.

What is the meaning of conditional statements?

Conditional statements, also known as “if-then” statements, express a cause-and-effect or logical relationship between two propositions.

When does the truth value of a conditional statement is F?

A conditional statement is considered false when the antecedent is true and the consequent is false.

What is the contrapositive of a conditional statement?

The contrapositive reverses the order of the statements and also negates both the statements. It is equivalent in truth value to the original statement.

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Conditional Statement

A conditional statement is a part of mathematical reasoning which is a critical skill that enables students to analyze a given hypothesis without any reference to a particular context or meaning. In layman words, when a scientific inquiry or statement is examined, the reasoning is not based on an individual's opinion. Derivations and proofs need a factual and scientific basis. 

Mathematical critical thinking and logical reasoning are important skills that are required to solve maths reasoning questions.

In this mini-lesson, we will explore the world of conditional statements. We will walk through the answers to the questions like what is meant by a conditional statement, what are the parts of a conditional statement, and how to create conditional statements along with solved examples and interactive questions.

Lesson Plan  

What Is Meant By a Conditional Statement?

A statement that is of the form "If p, then q" is a conditional statement. Here 'p' refers to 'hypothesis' and 'q' refers to 'conclusion'.

For example, "If Cliff is thirsty, then she drinks water."

This is a conditional statement. It is also called an implication.

'\(\rightarrow\)' is the symbol used to represent the relation between two statements. For example, A\(\rightarrow\)B. It is known as the logical connector. It can be read as A implies B. 

Here are two more conditional statement examples

Example 1: If a number is divisible by 4, then it is divisible by 2.

Example 2: If today is Monday, then yesterday was Sunday.

What Are the Parts of a Conditional Statement?

Hypothesis (if) and Conclusion (then) are the two main parts that form a conditional statement.

Let us consider the above-stated example to understand the parts of a conditional statement.

Conditional Statement : If today is Monday, then yesterday was Sunday.

Hypothesis : "If today is Monday."

Conclusion : "Then yesterday was Sunday."

On interchanging the form of statement the relationship gets changed.

To check whether the statement is true or false here, we have subsequent parts of a conditional statement. They are:

• Contrapositive

Biconditional Statement

Let us consider hypothesis as statement A and Conclusion as statement B.

Following are the observations made:

Converse of Statement

When hypothesis and conclusion are switched or interchanged, it is termed as converse statement . For example,

Conditional Statement : “If today is Monday, then yesterday was Sunday.”

Hypothesis : “If today is Monday”

Converse : “If yesterday was Sunday, then today is Monday.”

Here the conditional statement logic is, If B, then A (B → A)

Inverse of Statement

When both the hypothesis and conclusion of the conditional statement are negative, it is termed as an inverse of the statement. For example,

Conditional Statement: “If today is Monday, then yesterday was Sunday”.

Inverse : “If today is not Monday, then yesterday was not Sunday.”

Here the conditional statement logic is, If not A, then not B (~A → ~B)

Contrapositive Statement

When the hypothesis and conclusion are negative and simultaneously interchanged, then the statement is contrapositive. For example,

Contrapositive: “If yesterday was not Sunday, then today is not Monday”

Here the conditional statement logic is, if not B, then not A (~B → ~A)

The statement is a biconditional statement when a statement satisfies both the conditions as true, being conditional and converse at the same time. For example,

Biconditional : “Today is Monday if and only if yesterday was Sunday.”

Here the conditional statement logic is, A if and only if B (A ↔ B)

How to Create Conditional Statements?

Here, the point to be kept in mind is that the 'If' and 'then' part must be true.

If a number is a perfect square , then it is even.

• 'If' part is a number that is a perfect square.

Think of 4 which is a perfect square.

This has become true.

• The 'then' part is that the number should be even. 4 is even.

This has also become true. 

Thus, we have set up a conditional statement.

Let us hypothetically consider two statements, statement A and statement B. Observe the truth table for the statements:

According to the table, only if the hypothesis (A) is true and the conclusion (B) is false then, A → B will be false, or else A → B will be true for all other conditions.

• A sentence needs to be either true or false, but not both, to be considered as a mathematically accepted statement.
• Any sentence which is either imperative or interrogative or exclamatory cannot be considered a mathematically validated statement. 
• A sentence containing one or many variables is termed as an open statement. An open statement can become a statement if the variables present in the sentence are replaced by definite values.

Solved Examples

Let us have a look at a few solved examples on conditional statements.

Identify the types of conditional statements.

There are four types of conditional statements:

• If condition
• If-else condition
• Nested if-else
• If-else ladder.

Ray tells "If the perimeter of a rectangle is 14, then its area is 10."

Which of the following could be the counterexamples? Justify your decision.

a) A rectangle with sides measuring 2 and 5

b) A rectangle with sides measuring 10 and 1

c) A rectangle with sides measuring 1 and 5

d) A rectangle with sides measuring 4 and 3

a) Rectangle with sides 2 and 5: Perimeter = 14 and area = 10

Both 'if' and 'then' are true.

b) Rectangle with sides 10 and 1: Perimeter = 22 and area = 10

'If' is false and 'then' is true.

c) Rectangle with sides 1 and 5: Perimeter = 12 and area = 5

Both 'if' and 'then' are false.

d) Rectangle with sides 4 and 3: Perimeter = 14 and area = 12

'If' is true and 'then' is false.

Joe examined the set of numbers {16, 27, 24} to check if they are the multiples of 3. He claimed that they are divisible by 9. Do you agree or disagree? Justify your answer.

Conditional statement : If a number is a multiple of 3, then it is divisible by 9.

Let us find whether the conditions are true or false.

a) 16 is not a multiple of 3. Thus, the condition is false. 

16 is not divisible by 9. Thus, the conclusion is false. 

b) 27 is a multiple of 3. Thus, the condition is true.

27 is divisible by 9. Thus, the conclusion is true. 

c) 24 is a multiple of 3. Thus the condition is true.

24 is not divisible by 9. Thus the conclusion is false.

Write the converse, inverse, and contrapositive statement for the following conditional statement. 

If you study well, then you will pass the exam.

The given statement is - If you study well, then you will pass the exam.

It is of the form, "If p, then q"

The converse statement is, "You will pass the exam if you study well" (if q, then p).

The inverse statement is, "If you do not study well then you will not pass the exam" (if not p, then not q).

The contrapositive statement is, "If you did not pass the exam, then you did not study well" (if not q, then not p).

Interactive Questions

Here are a few activities for you to practice. Select/Type your answer and click the "Check Answer" button to see the result.

Let's Summarize

The mini-lesson targeted the fascinating concept of the conditional statement. The math journey around conditional statements started with what a student already knew and went on to creatively crafting a fresh concept in the young minds. Done in a way that not only it is relatable and easy to grasp, but also will stay with them forever.

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At  Cuemath , our team of math experts is dedicated to making learning fun for our favorite readers, the students!

Through an interactive and engaging learning-teaching-learning approach, the teachers explore all angles of a topic.

Be it worksheets, online classes, doubt sessions, or any other form of relation, it’s the logical thinking and smart learning approach that we, at Cuemath, believe in.

FAQs on Conditional Statement

1. what is the most common conditional statement.

'If and then' is the most commonly used conditional statement.

2. When do you use a conditional statement?

Conditional statements are used to justify the given condition or two statements as true or false.

3. What is if and if-else statement?

If is used when a specified condition is true. If-else is used when a particular specified condition is not satisfying and is false.

4. What is the symbol for a conditional statement?

'\(\rightarrow\)' is the symbol used to represent the relation between two statements. For example, A\(\rightarrow\)B. It is known as the logical connector. It can be read as A implies B.

5. What is the Contrapositive of a conditional statement?

If not B, then not A (~B → ~A)

6. What is a universal conditional statement?

Conditional statements are those statements where a hypothesis is followed by a conclusion. It is also known as an " If-then" statement. If the hypothesis is true and the conclusion is false, then the conditional statement is false. Likewise, if the hypothesis is false the whole statement is false. Conditional statements are also termed as implications.

Conditional Statement: If today is Monday, then yesterday was Sunday

Hypothesis: "If today is Monday."

Conclusion: "Then yesterday was Sunday."

If A, then B (A → B)

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Calcworkshop

Conditional Statement If Then's Defined in Geometry - 15+ Examples!

// Last Updated: January 21, 2020 - Watch Video //

In today’s geometry lesson , you’re going to learn all about conditional statements!

Jenn, Founder Calcworkshop ® , 15+ Years Experience (Licensed & Certified Teacher)

We’re going to walk through several examples to ensure you know what you’re doing.

In addition, this lesson will prepare you for deductive reasoning and two column proofs later on.

Here we go!

What are Conditional Statements?

To better understand deductive reasoning, we must first learn about conditional statements.

A conditional statement has two parts: hypothesis ( if ) and conclusion ( then ).

In fact, conditional statements are nothing more than “If-Then” statements!

Sometimes a picture helps form our hypothesis or conclusion. Therefore, we sometimes use Venn Diagrams to visually represent our findings and aid us in creating conditional statements.

But to verify statements are correct, we take a deeper look at our if-then statements. This is why we form the converse , inverse , and contrapositive of our conditional statements.

What is the Converse of a Statement?

Well, the converse is when we switch or interchange our hypothesis and conclusion.

Conditional Statement : “If today is Wednesday, then yesterday was Tuesday.”

Hypothesis : “If today is Wednesday” so our conclusion must follow “Then yesterday was Tuesday.”

So the converse is found by rearranging the hypothesis and conclusion, as Math Planet accurately states.

Converse : “If yesterday was Tuesday, then today is Wednesday.”

What is the Inverse of a Statement?

Now the inverse of an If-Then statement is found by negating (making negative) both the hypothesis and conclusion of the conditional statement.

So using our current conditional statement, “If today is Wednesday, then yesterday was Tuesday”.

Inverse : “If today is not Wednesday, then yesterday was not Tuesday.”

What is a Contrapositive?

And the contrapositive is formed by interchanging the hypothesis and conclusion and then negating both.

Contrapositive : “If yesterday was not Tuesday, then today is not Wednesday”

What is a Biconditional Statement?

A statement written in “if and only if” form combines a reversible statement and its true converse. In other words the conditional statement and converse are both true.

Continuing with our initial condition, “If today is Wednesday, then yesterday was Tuesday.”

Biconditional : “Today is Wednesday if and only if yesterday was Tuesday.”

Examples of Conditional Statements

In the video below we will look at several harder examples of how to form a proper statement, converse, inverse, and contrapositive. And here’s a big hint…

Whenever you see “con” that means you switch! It’s like being a con-artist!

Moreover, we will detail the process for coming up with reasons for our conclusions using known postulates. We will review the ten postulates that we have learned so far, and add a few more problems dealing with perpendicular lines, planes, and perpendicular bisectors.

After this lesson, we will be ready to tackle deductive reasoning head-on, and feel confident as we march onward toward learning two-column proofs!

Conditional Statements – Lesson & Examples (Video)

• Introduction to conditional statements
• 00:00:25 – What are conditional statements, converses, and biconditional statements? (Examples #1-2)
• 00:05:21 – Understanding venn diagrams (Examples #3-4)
• 00:11:07 – Supply the missing venn diagram and conditional statement for each question (Examples #5-8)
• Exclusive Content for Member’s Only
• 00:17:48 – Write the statement and converse then determine if they are reversible (Examples #9-12)
• 00:29:17 – Understanding the inverse, contrapositive, and symbol notation
• 00:35:33 – Write the statement, converse, inverse, contrapositive, and biconditional statements for each question (Examples #13-14)
• 00:45:40 – Using geometry postulates to verify statements (Example #15)
• 00:53:23 – What are perpendicular lines, perpendicular planes and the perpendicular bisector?
• 00:56:26 – Using the figure, determine if the statement is true or false (Example #16)
• Practice Problems with Step-by-Step Solutions
• Chapter Tests with Video Solutions

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Converse, Inverse, and Contrapositive

As your English teacher would say, good writers vary their sentence structure. The same is true of conditional statements: after a while, the If-Then formula becomes a real snoozefest. Some ways to mix it up are: "All things satisfying hypothesis are conclusion " and " Conclusion whenever hypothesis ."

However, mathematicians can be drier than the Sahara desert: they tend to write conditional statements as a formula p → q , where p is the hypothesis and q the conclusion. In fact, the old saying, "Mind your p 's and q 's," has its origins in this sort of mathematical logic.

Sample Problem

Identify p and q in the following statements, translating them into p → q form.

(A) If it rains outside, then flowers will grow tomorrow. (B) I cut off a finger whenever I peel rutabagas. (C) All dogs go to heaven.

For (A), p = "it rains outside" and q = "flowers will grow tomorrow."

In (B), we may rewrite the statement as "If I peel rutabagas, then I cut off a finger," telling us that p = "I peel rutabagas" and q = "I cut off a finger."

Finally, we may rewrite (C) as "If it is a dog, then it will go to heaven," yielding p = "it is a dog" and q = "it will go to heaven."

The hypothesis and conclusion play very different roles in conditional statements. Duh. In other words, p → q and q → p mean very different things. It's kind of like subtraction: 5 – 3 gives a different answer than 3 – 5. To highlight this distinction, mathematicians have given a special name to the statement q → p : it's called the converse of p → q .

No, not those Converse.

Write the converse of the statement, "If something is a watermelon, then it has seeds."

We want to switch the hypothesis and the conclusion, which will give us: "If something has seeds, then it is a watermelon." Of course, this converse is obviously false, since apples, cucumbers, and sunflowers all have seeds and are not watermelons. At least not during their day jobs.

There are some other special ways of modifying implications. For example, if you negate (that means stick a "not" in front of) both the hypothesis and conclusion, you get the inverse : in symbols, not p → not q is the inverse of p → q . Sometimes mathematicians like to be even more brief than this, so they'll abbreviate "not" with the symbol "~". So we can also write the inverse of p → q as ~ p → ~ q .

Finally, if you negate everything and flip p and q (taking the inverse of the converse, if you're fond of wordplay) then you get the contrapositive . Again in symbols, the contrapositive of p → q is the statement not q → not p , or ~ q → ~ p . Fancy.

What is the inverse of the statement "All mirrors are shiny?" What is its contrapositive?

If we abbreviate the first statement as mirror → shiny, then the inverse would be not mirror → not shiny and the contrapositive would be not shiny → not mirror. Written in English, the inverse is, "If it is not a mirror, then it is not shiny," while the contrapositive is, "If it is not shiny, then it is not a mirror."

While we've seen that it's possible for a statement to be true while its converse is false, it turns out that the contrapositive is better behaved. Whenever a conditional statement is true, its contrapositive is also true and vice versa. Similarly, a statement's converse and its inverse are always either both true or both false. (Note that the inverse is the contrapositive of the converse. Can you show that?)

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If-then statement

• Logical correct I
• Logical correct II

When we previously discussed inductive reasoning we based our reasoning on examples and on data from earlier events. If we instead use facts, rules and definitions then it's called deductive reasoning.

We will explain this by using an example.

If you get good grades then you will get into a good college.

The part after the "if": you get good grades - is called a hypotheses and the part after the "then" - you will get into a good college - is called a conclusion.

Hypotheses followed by a conclusion is called an If-then statement or a conditional statement.

This is noted as

$$p \to q$$

This is read - if p then q.

A conditional statement is false if hypothesis is true and the conclusion is false. The example above would be false if it said "if you get good grades then you will not get into a good college".

If we re-arrange a conditional statement or change parts of it then we have what is called a related conditional.

Our conditional statement is: if a population consists of 50% men then 50% of the population must be women.

If we exchange the position of the hypothesis and the conclusion we get a converse statemen t: if a population consists of 50% women then 50% of the population must be men.

$$q\rightarrow p$$

If both statements are true or if both statements are false then the converse is true. A conditional and its converse do not mean the same thing

If we negate both the hypothesis and the conclusion we get a inverse statemen t: if a population do not consist of 50% men then the population do not consist of 50% women.

$$\sim p\rightarrow \: \sim q$$

The inverse is not true juest because the conditional is true. The inverse always has the same truth value as the converse.

We could also negate a converse statement, this is called a contrapositive statemen t:  if a population do not consist of 50% women then the population do not consist of 50% men.

$$\sim q\rightarrow \: \sim p$$

The contrapositive does always have the same truth value as the conditional. If the conditional is true then the contrapositive is true.

A pattern of reaoning is a true assumption if it always lead to a true conclusion. The most common patterns of reasoning are detachment and syllogism.

If we turn of the water in the shower, then the water will stop pouring.

If we call the first part p and the second part q then we know that p results in q. This means that if p is true then q will also be true. This is called the law of detachment and is noted:

$$\left [ (p \to q)\wedge p \right ] \to q$$

The law of syllogism tells us that if p → q and q → r then p → r is also true.

This is noted:

$$\left [ (p \to q)\wedge (q \to r ) \right ] \to (p \to r)$$

If the following statements are true:

If we turn of the water (p), then the water will stop pouring (q). If the water stops pouring (q) then we don't get wet any more (r).

Then the law of syllogism tells us that if we turn of the water (p) then we don't get wet (r) must be true.

Video lesson

Write a converse, inverse and contrapositive to the conditional

"If you eat a whole pint of ice cream, then you won't be hungry"

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Logic Laws: Converse, Inverse, Contrapositive & Counterexample

Logical statements.

Logical statements  are utterances that can be tested for truth or falsity. The phrase, "Jennifer's white birds" is not a logical statement because it lacks meaning. The phrase, "Jennifer is best at magic" is not logical because it is an opinion; it is not testable. The phrase, "Jennifer wears dresses every Tuesday" is logical because it can be tested. Either she wears dresses on Tuesdays or she does not.

Humans are not born to be logical. Most humans do not begin to learn logic until they are around 10 years old. Logic is a learned mathematical skill, a method of ferreting out truth using specific steps and formal structures. Some of those structures of formal logic are converse, inverse, contrapositive, and counterexample statements.

Logical statements must be tested to be valid. For example, one of the two statements below is logical in that they can be tested for its truthfulness. One is an opinion, which cannot be tested for truthfulness:

Cuban food tastes best.

Jennifer is a man.

The first statement is an opinion and is neither logical nor factual; it cannot be tested to be true. We know the second statement can be tested for its truthfulness. The second statement is logical but not factual.

Logic statement examples

Which of these phrases or utterances is a logical statement? Remember, it need not be true, just testable.

Mint chocolate chip ice cream is delicious.

Jennifer is a woman.

3 penguins and 2 water buffalo

Fricasé de Pollo is a type of Cuban food.

Statements 2 and 4 are logical statements; statement 1 is an opinion, and statement 3 is a fragment with no logical meaning.

Four testable types of logical statements are  converse, inverse, contrapositive, and counterexample statements . They can produce logical equivalence for the original statement, but they do not  necessarily  produce logical equivalence.

Logical equivalence

Suppose instead of writing an opinion for our first statement and a logical but not factual second statement, we wrote:

Jennifer is alive.

Living women eat food.

We can use these statements to form a  conditional statement  with a hypothesis and a conclusion:

If Jennifer is alive, then Jennifer eats food.

This type of if-then statement is the heart of logic. We can immediately see that the two statements result in a true conditional statement. Here is Jennifer; she is alive; she eats food (and we already know she likes Cuban food).

A  logical equivalence  recasts the same hypothesis and conclusion as a negative statement that produces the same result:

If Jennifer does not eat food, then Jennifer is not alive.

These statements have logical equivalence because they contain the same content and arrive at the same result. Statements with logical equivalence are either both true or both false.

Converse statements

The original if-then conditional statement was:

Switching the hypothesis for the conclusion provides the  converse statement :

If Jennifer eats food, then Jennifer is alive.

We have the same words, but the order of the two parts has changed. Has the truth of the conditional statement changed? In this case, the statement is still true, but it would not have to be true.

Switching the conclusion for the hypothesis does not  automatically  prove the logical conditional statement, so the converse statement could be true  or  false.

Inverse statements

A logical  inverse statement  negates both the hypothesis and the conclusion. Again, our original, conditional statement was:

By carefully making the hypothesis negative and then negating the conclusion, we create the inverse statement:

The inverse statement may or  may not  be true.

Let's compare the converse and inverse statements to see if we can make any judgments about them:

Converse: If Jennifer eats food, then Jennifer is alive.

Inverse: If Jennifer is not alive, then Jennifer does not eat food.

Both of those produce true statements. Neither would have to produce a true statement, but in this case they did. It is not possible for one to produce a true statement and the other to produce a false statement.

We now know these three facts about converse and inverse statements:

If one is true, the other statement is true.

If one statement is false, the other is false.

Converse and inverse statements are logically equivalent to one another.

Contrapositive statements

If the converse reverses a statement and the inverse negates it, could we do both? Could we flip  and  negate the statement?

Our original conditional statement was:

To create the logical  contrapositive statement , we negate the hypothesis and the conclusion and then we also switch them:

If the conditional statement is true, then the logical contrapositive statement is true. If the logical contrapositive statement is false, then the conditional statement itself is also false. They have logical equivalence.

Counterexamples

If you can find a substitute that tests the logical validity of the statement (but not its factual accuracy), you know the claim is not always true and is therefore not logically valid.

We need only find one instance, called a  counterexample , where the conditions set out in our arguments are not valid:

Original statement: If Jennifer is alive, then Jennifer eats food.

Contrapositive: If Jennifer does not eat food, then Jennifer is not alive.

We would need to find a single example of one of these conditions, any one of which would be a counterexample:

A living woman who does not eat food, or

A woman who eats food but who is not alive, or

A nonliving woman who eats food, or

A woman who does not eat food but who is alive

If we can find such an example, even a single example, in which the premises are valid but the conclusions are false, we would have a counterexample showing the original argument is invalid.

Surely you can see - leaving out zombies and vampires and other imaginary creatures - that we cannot produce a counterexample for any of our logical statements; our argument is valid.

The logical result of all this work with converse, inverse, contrapositive, and counterexample logical statements is, we learn that Jennifer is a living, breathing woman who eats.

And she likes Cuban food!

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• 1. Multiple Choice Edit 1 minute 1 pt When taking the converse of a statement we_____ the hypothesis and the conclusion. negate highlight switch switch and negate
• 2. Multiple Choice Edit 1 minute 1 pt When taking the inverse of a statement, we_______ the hypothesis and the conclusion. negate switch switch and negate keep 
• 3. Multiple Choice Edit 1 minute 1 pt Conditional: If it does not rain, then we will have practice. What is "If it rains today, then we will have not practice", called Inverse Converse Contrapositive Counterexample
• 4. Multiple Choice Edit 1 minute 1 pt Given: If I have a Siberian Husky, then I have a dog Identify the contrapositive If I do not have a Siberian Husky, then I do not have a dog If I do not have a dog, then I do not have a Siberian Husky. If I have a dog, then I have a Siberian Husky If I have a dog, then I don't have a Siberian Husky
• 5. Multiple Choice Edit 1 minute 1 pt If two angles are supplementary, then they have a sum of 180. Is the inverse true? Yes, if two angles are not supplementary, then they do not add to 180 no, if angles don't add to 180, they can still be supplementary no, Supplementary angles add to 90 yes, supplementary angles add to less than 180
• 6. Multiple Choice Edit 1 minute 1 pt Conditional : If you are a duck, then you drive a red car. What is this statement called: If you do not drive a red car, then you are not a duck. Conditional Converse Inverse Contrapositive
• 7. Multiple Choice Edit 1 minute 1 pt If it is a number, then it is either positive or negative. What is an appropriate counterexample? 10 -2 0 True. There is no counterexample.
• 9. Multiple Choice Edit 1 minute 1 pt Original: If Jenny buys a guitar, then she will not buy a keyboard. What is this? If Jenny does buy a keyboard, then she will not buy a guitar. Converse Inverse Contrapositive Negation
• 10. Multiple Choice Edit 1 minute 1 pt Original: If Emily is not late to class, then she will not be marked tardy. What is this? If Emily is late to class, then she will be marked tardy. Converse Inverse Contrapositive Negation
• 11. Multiple Choice Edit 1 minute 1 pt A type of logical statement that has 2 parts, a hypothesis and conclusion is called a __________________________________. Conditional Statement Conjecture Counterexample Proof
• 12. Multiple Choice Edit 1 minute 1 pt A specific case that shows a conjecture is false is called a _______________________________. Counterexample Conjecture Inverse Negation
• 13. Multiple Choice Edit 1 minute 1 pt A process that includes looking for patterns and making conjectures is called _______________________. Inductive Reasoning Deductive Reasoning Conjecture Conditional Statement

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What Is a Hypothesis and How Do I Write One?

General Education

Think about something strange and unexplainable in your life. Maybe you get a headache right before it rains, or maybe you think your favorite sports team wins when you wear a certain color. If you wanted to see whether these are just coincidences or scientific fact, you would form a hypothesis, then create an experiment to see whether that hypothesis is true or not.

But what is a hypothesis, anyway? If you’re not sure about what a hypothesis is--or how to test for one!--you’re in the right place. This article will teach you everything you need to know about hypotheses, including: 

• Defining the term “hypothesis” 
• Providing hypothesis examples 
• Giving you tips for how to write your own hypothesis

So let’s get started!

What Is a Hypothesis?

Merriam Webster defines a hypothesis as “an assumption or concession made for the sake of argument.” In other words, a hypothesis is an educated guess . Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it’s true or not. Keep in mind that in science, a hypothesis should be testable. You have to be able to design an experiment that tests your hypothesis in order for it to be valid. 

As you could assume from that statement, it’s easy to make a bad hypothesis. But when you’re holding an experiment, it’s even more important that your guesses be good...after all, you’re spending time (and maybe money!) to figure out more about your observation. That’s why we refer to a hypothesis as an educated guess--good hypotheses are based on existing data and research to make them as sound as possible.

Hypotheses are one part of what’s called the scientific method .  Every (good) experiment or study is based in the scientific method. The scientific method gives order and structure to experiments and ensures that interference from scientists or outside influences does not skew the results. It’s important that you understand the concepts of the scientific method before holding your own experiment. Though it may vary among scientists, the scientific method is generally made up of six steps (in order):

• Observation
• Asking questions
• Forming a hypothesis
• Analyze the data
• Communicate your results

You’ll notice that the hypothesis comes pretty early on when conducting an experiment. That’s because experiments work best when they’re trying to answer one specific question. And you can’t conduct an experiment until you know what you’re trying to prove!

Independent and Dependent Variables 

After doing your research, you’re ready for another important step in forming your hypothesis: identifying variables. Variables are basically any factor that could influence the outcome of your experiment . Variables have to be measurable and related to the topic being studied.

There are two types of variables:  independent variables and dependent variables. I ndependent variables remain constant . For example, age is an independent variable; it will stay the same, and researchers can look at different ages to see if it has an effect on the dependent variable. 

Speaking of dependent variables... dependent variables are subject to the influence of the independent variable , meaning that they are not constant. Let’s say you want to test whether a person’s age affects how much sleep they need. In that case, the independent variable is age (like we mentioned above), and the dependent variable is how much sleep a person gets. 

Variables will be crucial in writing your hypothesis. You need to be able to identify which variable is which, as both the independent and dependent variables will be written into your hypothesis. For instance, in a study about exercise, the independent variable might be the speed at which the respondents walk for thirty minutes, and the dependent variable would be their heart rate. In your study and in your hypothesis, you’re trying to understand the relationship between the two variables.

Elements of a Good Hypothesis

The best hypotheses start by asking the right questions . For instance, if you’ve observed that the grass is greener when it rains twice a week, you could ask what kind of grass it is, what elevation it’s at, and if the grass across the street responds to rain in the same way. Any of these questions could become the backbone of experiments to test why the grass gets greener when it rains fairly frequently.

As you’re asking more questions about your first observation, make sure you’re also making more observations . If it doesn’t rain for two weeks and the grass still looks green, that’s an important observation that could influence your hypothesis. You'll continue observing all throughout your experiment, but until the hypothesis is finalized, every observation should be noted.

Finally, you should consult secondary research before writing your hypothesis . Secondary research is comprised of results found and published by other people. You can usually find this information online or at your library. Additionally, m ake sure the research you find is credible and related to your topic. If you’re studying the correlation between rain and grass growth, it would help you to research rain patterns over the past twenty years for your county, published by a local agricultural association. You should also research the types of grass common in your area, the type of grass in your lawn, and whether anyone else has conducted experiments about your hypothesis. Also be sure you’re checking the quality of your research . Research done by a middle school student about what minerals can be found in rainwater would be less useful than an article published by a local university.

Writing Your Hypothesis

Once you’ve considered all of the factors above, you’re ready to start writing your hypothesis. Hypotheses usually take a certain form when they’re written out in a research report.

When you boil down your hypothesis statement, you are writing down your best guess and not the question at hand . This means that your statement should be written as if it is fact already, even though you are simply testing it.

The reason for this is that, after you have completed your study, you'll either accept or reject your if-then or your null hypothesis. All hypothesis testing examples should be measurable and able to be confirmed or denied. You cannot confirm a question, only a statement! 

In fact, you come up with hypothesis examples all the time! For instance, when you guess on the outcome of a basketball game, you don’t say, “Will the Miami Heat beat the Boston Celtics?” but instead, “I think the Miami Heat will beat the Boston Celtics.” You state it as if it is already true, even if it turns out you’re wrong. You do the same thing when writing your hypothesis.

Additionally, keep in mind that hypotheses can range from very specific to very broad.  These hypotheses can be specific, but if your hypothesis testing examples involve a broad range of causes and effects, your hypothesis can also be broad.  

The Two Types of Hypotheses

Now that you understand what goes into a hypothesis, it’s time to look more closely at the two most common types of hypothesis: the if-then hypothesis and the null hypothesis.

#1: If-Then Hypotheses

First of all, if-then hypotheses typically follow this formula:

If ____ happens, then ____ will happen.

The goal of this type of hypothesis is to test the causal relationship between the independent and dependent variable. It’s fairly simple, and each hypothesis can vary in how detailed it can be. We create if-then hypotheses all the time with our daily predictions. Here are some examples of hypotheses that use an if-then structure from daily life: 

• If I get enough sleep, I’ll be able to get more work done tomorrow.
• If the bus is on time, I can make it to my friend’s birthday party. 
• If I study every night this week, I’ll get a better grade on my exam. 

In each of these situations, you’re making a guess on how an independent variable (sleep, time, or studying) will affect a dependent variable (the amount of work you can do, making it to a party on time, or getting better grades). 

You may still be asking, “What is an example of a hypothesis used in scientific research?” Take one of the hypothesis examples from a real-world study on whether using technology before bed affects children’s sleep patterns. The hypothesis read s:

“We hypothesized that increased hours of tablet- and phone-based screen time at bedtime would be inversely correlated with sleep quality and child attention.”

It might not look like it, but this is an if-then statement. The researchers basically said, “If children have more screen usage at bedtime, then their quality of sleep and attention will be worse.” The sleep quality and attention are the dependent variables and the screen usage is the independent variable. (Usually, the independent variable comes after the “if” and the dependent variable comes after the “then,” as it is the independent variable that affects the dependent variable.) This is an excellent example of how flexible hypothesis statements can be, as long as the general idea of “if-then” and the independent and dependent variables are present.

#2: Null Hypotheses

Your if-then hypothesis is not the only one needed to complete a successful experiment, however. You also need a null hypothesis to test it against. In its most basic form, the null hypothesis is the opposite of your if-then hypothesis . When you write your null hypothesis, you are writing a hypothesis that suggests that your guess is not true, and that the independent and dependent variables have no relationship .

One null hypothesis for the cell phone and sleep study from the last section might say: 

“If children have more screen usage at bedtime, their quality of sleep and attention will not be worse.” 

In this case, this is a null hypothesis because it’s asking the opposite of the original thesis! 

Conversely, if your if-then hypothesis suggests that your two variables have no relationship, then your null hypothesis would suggest that there is one. So, pretend that there is a study that is asking the question, “Does the amount of followers on Instagram influence how long people spend on the app?” The independent variable is the amount of followers, and the dependent variable is the time spent. But if you, as the researcher, don’t think there is a relationship between the number of followers and time spent, you might write an if-then hypothesis that reads:

“If people have many followers on Instagram, they will not spend more time on the app than people who have less.”

In this case, the if-then suggests there isn’t a relationship between the variables. In that case, one of the null hypothesis examples might say:

“If people have many followers on Instagram, they will spend more time on the app than people who have less.”

You then test both the if-then and the null hypothesis to gauge if there is a relationship between the variables, and if so, how much of a relationship. 

4 Tips to Write the Best Hypothesis

If you’re going to take the time to hold an experiment, whether in school or by yourself, you’re also going to want to take the time to make sure your hypothesis is a good one. The best hypotheses have four major elements in common: plausibility, defined concepts, observability, and general explanation.

#1: Plausibility

At first glance, this quality of a hypothesis might seem obvious. When your hypothesis is plausible, that means it’s possible given what we know about science and general common sense. However, improbable hypotheses are more common than you might think. 

Imagine you’re studying weight gain and television watching habits. If you hypothesize that people who watch more than  twenty hours of television a week will gain two hundred pounds or more over the course of a year, this might be improbable (though it’s potentially possible). Consequently, c ommon sense can tell us the results of the study before the study even begins.

Improbable hypotheses generally go against  science, as well. Take this hypothesis example: 

“If a person smokes one cigarette a day, then they will have lungs just as healthy as the average person’s.” 

This hypothesis is obviously untrue, as studies have shown again and again that cigarettes negatively affect lung health. You must be careful that your hypotheses do not reflect your own personal opinion more than they do scientifically-supported findings. This plausibility points to the necessity of research before the hypothesis is written to make sure that your hypothesis has not already been disproven.

#2: Defined Concepts

The more advanced you are in your studies, the more likely that the terms you’re using in your hypothesis are specific to a limited set of knowledge. One of the hypothesis testing examples might include the readability of printed text in newspapers, where you might use words like “kerning” and “x-height.” Unless your readers have a background in graphic design, it’s likely that they won’t know what you mean by these terms. Thus, it’s important to either write what they mean in the hypothesis itself or in the report before the hypothesis.

Here’s what we mean. Which of the following sentences makes more sense to the common person?

If the kerning is greater than average, more words will be read per minute.

If the space between letters is greater than average, more words will be read per minute.

For people reading your report that are not experts in typography, simply adding a few more words will be helpful in clarifying exactly what the experiment is all about. It’s always a good idea to make your research and findings as accessible as possible. 

Good hypotheses ensure that you can observe the results. 

#3: Observability

In order to measure the truth or falsity of your hypothesis, you must be able to see your variables and the way they interact. For instance, if your hypothesis is that the flight patterns of satellites affect the strength of certain television signals, yet you don’t have a telescope to view the satellites or a television to monitor the signal strength, you cannot properly observe your hypothesis and thus cannot continue your study.

Some variables may seem easy to observe, but if you do not have a system of measurement in place, you cannot observe your hypothesis properly. Here’s an example: if you’re experimenting on the effect of healthy food on overall happiness, but you don’t have a way to monitor and measure what “overall happiness” means, your results will not reflect the truth. Monitoring how often someone smiles for a whole day is not reasonably observable, but having the participants state how happy they feel on a scale of one to ten is more observable. 

In writing your hypothesis, always keep in mind how you'll execute the experiment.

#4: Generalizability 

Perhaps you’d like to study what color your best friend wears the most often by observing and documenting the colors she wears each day of the week. This might be fun information for her and you to know, but beyond you two, there aren’t many people who could benefit from this experiment. When you start an experiment, you should note how generalizable your findings may be if they are confirmed. Generalizability is basically how common a particular phenomenon is to other people’s everyday life.

Let’s say you’re asking a question about the health benefits of eating an apple for one day only, you need to realize that the experiment may be too specific to be helpful. It does not help to explain a phenomenon that many people experience. If you find yourself with too specific of a hypothesis, go back to asking the big question: what is it that you want to know, and what do you think will happen between your two variables?

Hypothesis Testing Examples

We know it can be hard to write a good hypothesis unless you’ve seen some good hypothesis examples. We’ve included four hypothesis examples based on some made-up experiments. Use these as templates or launch pads for coming up with your own hypotheses.

Experiment #1: Students Studying Outside (Writing a Hypothesis)

You are a student at PrepScholar University. When you walk around campus, you notice that, when the temperature is above 60 degrees, more students study in the quad. You want to know when your fellow students are more likely to study outside. With this information, how do you make the best hypothesis possible?

You must remember to make additional observations and do secondary research before writing your hypothesis. In doing so, you notice that no one studies outside when it’s 75 degrees and raining, so this should be included in your experiment. Also, studies done on the topic beforehand suggested that students are more likely to study in temperatures less than 85 degrees. With this in mind, you feel confident that you can identify your variables and write your hypotheses:

If-then: “If the temperature in Fahrenheit is less than 60 degrees, significantly fewer students will study outside.”

Null: “If the temperature in Fahrenheit is less than 60 degrees, the same number of students will study outside as when it is more than 60 degrees.”

These hypotheses are plausible, as the temperatures are reasonably within the bounds of what is possible. The number of people in the quad is also easily observable. It is also not a phenomenon specific to only one person or at one time, but instead can explain a phenomenon for a broader group of people.

To complete this experiment, you pick the month of October to observe the quad. Every day (except on the days where it’s raining)from 3 to 4 PM, when most classes have released for the day, you observe how many people are on the quad. You measure how many people come  and how many leave. You also write down the temperature on the hour. 

After writing down all of your observations and putting them on a graph, you find that the most students study on the quad when it is 70 degrees outside, and that the number of students drops a lot once the temperature reaches 60 degrees or below. In this case, your research report would state that you accept or “failed to reject” your first hypothesis with your findings.

Experiment #2: The Cupcake Store (Forming a Simple Experiment)

Let’s say that you work at a bakery. You specialize in cupcakes, and you make only two colors of frosting: yellow and purple. You want to know what kind of customers are more likely to buy what kind of cupcake, so you set up an experiment. Your independent variable is the customer’s gender, and the dependent variable is the color of the frosting. What is an example of a hypothesis that might answer the question of this study?

Here’s what your hypotheses might look like: 

If-then: “If customers’ gender is female, then they will buy more yellow cupcakes than purple cupcakes.”

Null: “If customers’ gender is female, then they will be just as likely to buy purple cupcakes as yellow cupcakes.”

This is a pretty simple experiment! It passes the test of plausibility (there could easily be a difference), defined concepts (there’s nothing complicated about cupcakes!), observability (both color and gender can be easily observed), and general explanation ( this would potentially help you make better business decisions ).

Experiment #3: Backyard Bird Feeders (Integrating Multiple Variables and Rejecting the If-Then Hypothesis)

While watching your backyard bird feeder, you realized that different birds come on the days when you change the types of seeds. You decide that you want to see more cardinals in your backyard, so you decide to see what type of food they like the best and set up an experiment. 

However, one morning, you notice that, while some cardinals are present, blue jays are eating out of your backyard feeder filled with millet. You decide that, of all of the other birds, you would like to see the blue jays the least. This means you'll have more than one variable in your hypothesis. Your new hypotheses might look like this: 

If-then: “If sunflower seeds are placed in the bird feeders, then more cardinals will come than blue jays. If millet is placed in the bird feeders, then more blue jays will come than cardinals.”

Null: “If either sunflower seeds or millet are placed in the bird, equal numbers of cardinals and blue jays will come.”

Through simple observation, you actually find that cardinals come as often as blue jays when sunflower seeds or millet is in the bird feeder. In this case, you would reject your “if-then” hypothesis and “fail to reject” your null hypothesis . You cannot accept your first hypothesis, because it’s clearly not true. Instead you found that there was actually no relation between your different variables. Consequently, you would need to run more experiments with different variables to see if the new variables impact the results.

Experiment #4: In-Class Survey (Including an Alternative Hypothesis)

You’re about to give a speech in one of your classes about the importance of paying attention. You want to take this opportunity to test a hypothesis you’ve had for a while: 

If-then: If students sit in the first two rows of the classroom, then they will listen better than students who do not.

Null: If students sit in the first two rows of the classroom, then they will not listen better or worse than students who do not.

You give your speech and then ask your teacher if you can hand out a short survey to the class. On the survey, you’ve included questions about some of the topics you talked about. When you get back the results, you’re surprised to see that not only do the students in the first two rows not pay better attention, but they also scored worse than students in other parts of the classroom! Here, both your if-then and your null hypotheses are not representative of your findings. What do you do?

This is when you reject both your if-then and null hypotheses and instead create an alternative hypothesis . This type of hypothesis is used in the rare circumstance that neither of your hypotheses is able to capture your findings . Now you can use what you’ve learned to draft new hypotheses and test again! 

Key Takeaways: Hypothesis Writing

The more comfortable you become with writing hypotheses, the better they will become. The structure of hypotheses is flexible and may need to be changed depending on what topic you are studying. The most important thing to remember is the purpose of your hypothesis and the difference between the if-then and the null . From there, in forming your hypothesis, you should constantly be asking questions, making observations, doing secondary research, and considering your variables. After you have written your hypothesis, be sure to edit it so that it is plausible, clearly defined, observable, and helpful in explaining a general phenomenon.

Writing a hypothesis is something that everyone, from elementary school children competing in a science fair to professional scientists in a lab, needs to know how to do. Hypotheses are vital in experiments and in properly executing the scientific method . When done correctly, hypotheses will set up your studies for success and help you to understand the world a little better, one experiment at a time.

What’s Next?

If you’re studying for the science portion of the ACT, there’s definitely a lot you need to know. We’ve got the tools to help, though! Start by checking out our ultimate study guide for the ACT Science subject test. Once you read through that, be sure to download our recommended ACT Science practice tests , since they’re one of the most foolproof ways to improve your score. (And don’t forget to check out our expert guide book , too.)

If you love science and want to major in a scientific field, you should start preparing in high school . Here are the science classes you should take to set yourself up for success.

If you’re trying to think of science experiments you can do for class (or for a science fair!), here’s a list of 37 awesome science experiments you can do at home

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Ashley Sufflé Robinson has a Ph.D. in 19th Century English Literature. As a content writer for PrepScholar, Ashley is passionate about giving college-bound students the in-depth information they need to get into the school of their dreams.

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• Introduction
• Conclusions
• Article Information

“Did not complete study” indicates did not complete all phases of the trial including the last visit (follow-up).

a Six participants withdrew consent and 1 was not rescreened per protocol.

b Three participants (0.8%) in OASIS 1 were randomized but did not receive treatment intervention and were, therefore, excluded from the safety analysis sets. One participant in the placebo group of each trial incorrectly received elinzanetant initially, and both were assigned to the elinzanetant groups for analyses based on the safety analysis set.

c No information provided by investigator.

d Withdrew because of work.

Score range 0-3 (0 indicates no moderate or severe vasomotor symptoms [at baseline] and no mild, moderate, or severe vasomotor symptoms [post baseline]; 1, mild; 2, moderate; and 3, severe symptoms; higher scores indicate greater vasomotor symptom severity). Data shown are means (95% CIs). Zoomed-in presentation along the y-axis for illustration purposes. Only outliers within the displayed scale are visible. Connecting lines over time join arithmetic means by treatment group. A boxplot presentation of the full distribution is provided in eFigure 1 in Supplement 3 and the descriptive summary and data range are available in eTable 5 in Supplement 3 . Placebo-elinzanetant, 120 mg, refers to the participants receiving placebo who were switched to receive elinzanetant after week 12. Efficacy analyses were performed on the full analysis set. The circles indicate outside values.

A reduction in Patient-Reported Outcomes Measurement Information System Sleep Disturbance Short Form 8b (PROMIS SD SF 8b) total T score and Menopause-Specific Quality of Life (MENQOL) questionnaire total score corresponded to an improvement in symptoms. Efficacy analyses were performed on the full analysis set. Connecting lines over time join arithmetic means by treatment group. The circles indicate outside values.

a For PROMIS SD SF 8b, the score ranges from 28.9 to 76.5 (<55, normal; 55-60, mild; 60-70, moderate; and >70, severe sleep disturbance). Higher scores indicate greater severity of sleep disturbance.

b For MENQOL, the score ranges from 1 to 8; higher scores indicate greater bother; and 0.9-point within-patient change represent a clinically meaningful difference.

Trial Protocol

Statistical Analysis Plan

eTable 1. OASIS 1 Sites Summary

eTable 2. OASIS 2 Sites Summary

eTable 3. Primary and Key Secondary Endpoints

eTable 4. Main Estimand: Intercurrent Events and Strategies to Address Them

eTable 5. Mean Change From Baseline in Average Daily Moderate-to-Severe Vasomotor Symptom Frequency Over Time by Treatment Arm and Study

eFigure 1. Change From Baseline in Average Daily Vasomotor Symptom Frequency in OASIS 1 (Top) and OASIS 2 (Bottom)

eFigure 2. Mean Change From Baseline in Average Daily Moderate-to-Severe Vasomotor Symptom Frequency Over Time in OASIS 1 (Top) and OASIS 2 (Bottom)

eTable 6. Percentage Change From Baseline in Average Daily Moderate-to-Severe Vasomotor Symptom Frequency Over Time by Treatment Arm and Study

eTable 7. Mean Change From Baseline in Average Daily Vasomotor Symptom Severity Over Time by Treatment Arm and Study

eFigure 3. Mean Change From Baseline in Average Daily Vasomotor Symptom Severity Over Time in OASIS 1 (Top) and OASIS 2 (Bottom)

eTable 8. Mean Change From Baseline in PROMIS SD SF 8b Total T-Scores Over Time by Treatment Arm and Study

eTable 9. Mean Change From Baseline in PROMIS SD SF 8b Total Raw Scores Over Time by Treatment Arm and Study

eFigure 4. Mean Change From Baseline in PROMIS SD SF 8b Total T-Scores Over Time in OASIS 1 (Top) and OASIS 2 (Bottom)

eTable 10. Mean Change From Baseline in MENQOL Total Score Over Time by Treatment Arm and Study

eFigure 5. Mean Change From Baseline in MENQOL Total Score Over Time in OASIS 1 (Top) and OASIS 2 (Bottom)

eTable 11. Treatment-Emergent Adverse Events in OASIS 1 by Treatment Arm and During Elinzanetant Exposure

eTable 12. Treatment-Emergent Adverse Events in OASIS 2 by Treatment Arm and During Elinzanetant Exposure

eTable 13. Summary of Treatment-Emergent Adverse Events During Elinzanetant Period (Weeks 13–26) and Treatment-Emergent Adverse Events During Elinzanetant Exposure (Weeks 1–26)

Data Sharing Statement

• A New Era in Menopause Management? JAMA Editorial August 22, 2024 Stephanie S. Faubion, MD, MBA; Chrisandra L. Shufelt, MD, MS

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Pinkerton JV , Simon JA , Joffe H, et al. Elinzanetant for the Treatment of Vasomotor Symptoms Associated With Menopause : OASIS 1 and 2 Randomized Clinical Trials . JAMA. Published online August 22, 2024. doi:10.1001/jama.2024.14618

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Elinzanetant for the Treatment of Vasomotor Symptoms Associated With Menopause : OASIS 1 and 2 Randomized Clinical Trials

• 1 Department of Obstetrics and Gynecology, Division Midlife Health, University of Virginia Health, Charlottesville
• 2 IntimMedicine Specialists, George Washington University, Washington, DC
• 3 Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
• 4 Department of Psychiatry, Psychology, and OB/GYN, University of Illinois at Chicago
• 5 Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, Italy
• 6 Research Center for Reproductive Medicine, Gynecological Endocrinology, and Menopause, IRCCS San Matteo Foundation, Pavia, Italy
• 7 Queen Charlotte’s and Chelsea Hospital, Imperial College London, London, United Kingdom
• 8 Department of Psychiatry, Queen’s University School of Medicine, Kingston, Ontario, Canada
• 9 Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
• 10 Bayer CC AG, Basel, Switzerland
• 11 Bayer AG, Berlin, Germany
• 12 Statistics and Data Sciences, Bayer PLC, Reading, United Kingdom
• 13 Bayer AG, Wuppertal, Germany
• 14 Charité–Universitätsmedizin Berlin, Germany
• Editorial A New Era in Menopause Management? Stephanie S. Faubion, MD, MBA; Chrisandra L. Shufelt, MD, MS JAMA

Question   What are the efficacy and safety of elinzanetant, 120 mg, in postmenopausal individuals with moderate to severe vasomotor symptoms (VMS)?

Findings   In 2 pivotal phase 3 clinical trials, elinzanetant demonstrated statistically significant reductions in VMS frequency and severity vs placebo. Elinzanetant also significantly improved sleep disturbances and menopause-related quality of life vs placebo; the safety profile was favorable.

Meaning   Elinzanetant is an efficacious and well-tolerated selective neurokinin-1,3 receptor antagonist for the treatment of moderate to severe VMS associated with menopause. Elinzanetant also improves sleep disturbances and menopause-related quality of life.

Importance   Safe and effective nonhormonal treatments for menopausal vasomotor symptoms (VMS) are needed.

Objective   To evaluate the efficacy and safety of elinzanetant, a selective neurokinin-1,3 receptor antagonist, for the treatment of moderate to severe menopausal vasomotor symptoms.

Design, Setting, and Participants   Two randomized double-blind phase 3 trials (OASIS 1 and 2) included postmenopausal participants aged 40 to 65 years experiencing moderate to severe vasomotor symptoms (OASIS 1: 77 sites in the US, Europe, and Israel from August 27, 2021, to November 27, 2023, and OASIS 2: 77 sites in the US, Canada, and Europe from October 29, 2021, to October 10, 2023).

Intervention   Once daily oral elinzanetant, 120 mg, for 26 weeks or matching placebo for 12 weeks followed by elinzanetant, 120 mg, for 14 weeks.

Main Outcomes and Measures   Primary end points included mean change in frequency and severity of moderate to severe vasomotor symptoms from baseline to weeks 4 and 12, measured by the electronic hot flash daily diary. Secondary end points included Patient-Reported Outcomes Measurement Information System Sleep Disturbance Short Form 8b total T score and Menopause-Specific Quality of Life questionnaire total score from baseline to week 12.

Results   Eligible participants (mean [SD] age, OASIS 1: 54.6 [4.9] years; OASIS 2: 54.6 [4.8] years) were randomized to elinzanetant (OASIS 1: n = 199; OASIS 2: n = 200) or placebo (OASIS 1: n = 197; OASIS 2: n = 200). A total of 309 (78.0%) and 324 (81.0%) completed OASIS 1 and 2, respectively. For the elinzanetant and placebo groups, the baseline mean (SD) VMS per 24 hours were 13.4 (6.6) vs 14.3 (13.9) (OASIS 1) and 14.7 (11.1) v 16.2 (11.2) (OASIS 2). Baseline VMS severity was 2.6 (0.2) vs 2.5 (0.2) (OASIS 1) and 2.5 (0.2) vs 2.5 (0.2) (OASIS 2). Elinzanetant significantly reduced VMS frequency at week 4 (OASIS 1: −3.3 [95% CI, −4.5 to −2.1], P  < .001; OASIS 2: −3.0 [95% CI, −4.4 to −1.7], P  < .001) and at week 12 (OASIS 1: −3.2 [95% CI, −4.8 to −1.6], P  < .001; OASIS 2: −3.2 [95% CI, −4.6 to −1.9], P  < .001). Elinzanetant also improved VMS severity at week 4 (OASIS 1: −0.3 [95% CI, −0.4 to −0.2], P  < .001; OASIS 2: −0.2 [95 CI, −0.3 to −0.1], P  < .001) and week 12 (OASIS 1: −0.4 [95% CI, −0.5 to −0.3], P  < .001; OASIS 2: −0.3 [95% CI, −0.4 to −0.1], P  < .001). Elinzanetant improved sleep disturbances and menopause-related quality of life at week 12, and the safety profile was favorable.

Conclusions and Relevance   Elinzanetant was well tolerated and efficacious for moderate to severe menopausal VMS.

Trial Registration   ClinicalTrials.gov Identifier: OASIS 1: NCT05042362 , OASIS 2: NCT05099159

Women experience a variety of symptoms during their menopausal transition, including vasomotor symptoms (VMS, also known as hot flashes) and sleep disturbances, reported by up to 80% and 60%, respectively. 1 - 3 Menopausal symptoms can negatively impact quality of life, reducing the capacity for daily activities and work productivity, 4 - 6 and may be associated with long-term negative health outcomes such as cardiovascular events, depressive symptoms, cognitive decline, and other adverse brain outcomes. 7 - 13

There are currently different treatment options that address the needs of individuals with menopausal symptoms. Hormone therapy and the selective serotonin reuptake inhibitor paroxetine salt, 7.5 mg, are approved for the treatment of VMS in some countries, while other selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors are used off label. 14 - 17 However, many women have contraindications, have tolerability issues leading to discontinuation, or prefer not to take these treatments. 4 , 17 , 18 Recently, the nonhormonal neurokinin (NK)-3 receptor antagonist fezolinetant was approved by the US Food and Drug Administration (FDA), European Commission, and Switzerland SwissMedic for the treatment of moderate to severe VMS due to menopause. 19 - 21

Hypothalamic kisspeptin/neurokinin/dynorphin (KNDy) neurons play a role in thermoregulation. 22 KNDy neurons express a number of receptor/ligand systems, including NK-1 and NK-3 receptors and their respective ligands, substance P (SP), and neurokinin B (NKB). Declining estrogen levels during and after the menopause transition lead to hypertrophy and hyperactivity of KNDy neurons, accompanied by elevated gene expression of neurotransmitters including NKB and SP. 22 - 25 This hyperactivation has been related to disruption of thermoregulation, which may trigger VMS. 22 Recent data showed the involvement of NK-3 receptors in this disruption; fezolinetant, an NK-3–specific receptor antagonist, demonstrated reductions in VMS in postmenopausal individuals. 26 , 27 SP and NK-1 receptors may have a role in peripheral vasodilatation and primary insomnia. 28 , 29

Elinzanetant is a nonhormonal compound in development for the treatment of VMS associated with menopause that specifically targets both NK-1 and NK-3 receptors. 30 , 31 Based on clinical data, it was hypothesized that the dual inhibition of NK-1 and NK-3 receptors would reduce VMS and may have an effect on sleep disturbances associated with menopause. 30 Indeed, elinzanetant demonstrated significant reductions in VMS frequency and severity compared with placebo, as well as improvements in different aspects of sleep and menopause-related quality of life in the phase 2b SWITCH-1 trial. 30 The aim of the OASIS 1 ( NCT05042362 ) and 2 ( NCT05099159 ) phase 3 randomized, placebo-controlled studies was to assess the efficacy and safety of elinzanetant, 120 mg, in individuals experiencing moderate to severe VMS associated with menopause.

OASIS 1 and 2 were pivotal, multicenter, multinational, double-blind, randomized, placebo-controlled, phase 3, 26-week intervention trials. The trials had similar designs and identical primary and key secondary end points in accordance with regulatory guidance to ensure the results were reliable and reproducible. 32 The trial protocol and statistical analysis plan are in Supplement 1 and Supplement 2 , respectively. The trials were conducted in parallel but involved different study sites mainly located in the US and Europe (eTables 1 and 2 in Supplement 3 ). The trials were conducted in accordance with the Declaration of Helsinki and the Council for International Organizations of Medical Sciences International Ethical Guidelines and reported using the CONSORT reporting guideline. Institutional review board and ethics committee approval were obtained for all study sites. Feedback from menopausal participants was collected to ensure that the patient voice was incorporated in aspects of the study design and assessments. 33 Detailed methodology of the OASIS 1 and 2 trials has been described in a previous publication. 33 Herein, we present the results from both OASIS 1 and 2 trials side by side to demonstrate the reproducibility of findings.

OASIS 1 and 2 included naturally or surgically (bilateral oophorectomy with or without hysterectomy) postmenopausal participants, aged 40 to 65 years and experiencing 50 or more moderate to severe VMS over 7 days during screening ( Table 1 and Figure 1 ). Exclusion criteria included abnormal liver parameters (including alanine aminotransferase or aspartate aminotransferase >2 × upper limit of normal), disordered proliferative endometrium, endometrial hyperplasia or polyps, and current or history of malignancy within the last 5 years (except basal and squamous cell skin tumors). 33 Written informed consent was obtained from all participants prior to study start. Race and ethnicity were determined during the visit by participant and site personnel together and documented in the case report form as part of the demographic characteristics using fixed categories (ethnicity: Hispanic or Latino, not Hispanic or Latino, not reported and race: American Indian or Alaska Native; Asian; Black or African American; Native Hawaiian or Other Pacific Islander; White; not reported).

In both trials, eligible participants were randomized in a 1:1 ratio to receive elinzanetant, 120 mg, or identical-appearing placebo once daily orally for 12 weeks. After 12 weeks, all participants received elinzanetant, 120 mg, for a further 14 weeks, followed by a 4-week posttreatment follow-up. Randomization was performed centrally using an interactive voice/web response system and stratified by North America and the rest of the countries. Investigators, participants, and study personnel remained blinded throughout the trials. The interactive voice/web response system was programmed with blind-breaking instructions in the event of an emergency.

The phase 3 end point strategy was developed in alignment with regulatory recommendations and clinical practice. 32 - 34 Efficacy end points in both trials were assessed using patient-reported outcome instruments collected on an electronic handheld device. Considerable research has been conducted to confirm the fitness for use of the patient-reported outcome instruments in assessing key efficacy end points in phase 3 VMS trials. 35 - 37 Primary and key secondary end points were identical across the OASIS 1 and 2 trials (eTable 3 in Supplement 3 ). The prespecified primary end points included mean change in frequency and severity of moderate to severe VMS from baseline to weeks 4 and 12, as measured by the electronic hot flash daily diary. The prespecified key secondary end points included mean change in moderate to severe VMS frequency from baseline to week 1 also measured by the hot flash daily diary, as well as mean changes in the Patient-Reported Outcomes Measurement Information System Sleep Disturbance Short Form (PROMIS SD SF) 8b total T score and the Menopause-Specific Quality of Life (MENQOL) questionnaire total score from baseline to week 12. The proportion of participants with at least a 50% reduction in VMS frequency at weeks 4 and 12 was an exploratory end point. For additional secondary and exploratory end points, see the statistical analysis plan in Supplement 2 (results not reported here).

The electronic hot flash daily diary was used to record the frequency and severity of VMS twice daily (in the morning when getting up and in the evening when going to bed) on a dedicated handheld device, similar to diaries used in other clinical trials in participants with VMS. 38 Participants were trained on the use of the handheld device for timely and accurate data entry during screening. Participants recorded whether or not they had hot flashes, followed by rating the total number of mild, moderate, and severe hot flashes they experienced. Mild hot flashes were defined as a sensation of heat without sweating, moderate as a sensation of heat with sweating but able to continue activity, and severe as a sensation of heat with sweating that causes cessation of activity. 32 Possible ranges were 0 to 180 for the VMS frequency score and 0 to 3 for the VMS severity score.

The PROMIS SD SF 8b questionnaire is a short form derived from the 27-item PROMIS SD item bank. 39 - 41 It assesses the degree of sleep disturbance over the past 7 days, with the 8 items particularly investigating restless sleep, satisfaction with sleep, refreshing sleep, difficulties falling asleep, staying asleep, getting to sleep, amount of sleep, and sleep quality. Items were scored on a 5-point Likert scale, and the 8 single item scores were summed to yield total raw scores (range, 8-40), which were converted to total T scores for analysis of the key secondary end point (range, 28.9-76.5). Higher scores indicated more disturbed sleep. A T score of 50 (SD, 10) represented the mean sleep disturbance score in a reference population. T scores of 55 or greater, 60 or greater, and 70 or greater represented mild, moderate, and severe levels of sleep disturbances, respectively, in the reference population. 42 , 43 Participants completed the questionnaire at baseline and weeks 1 through 4, 8, 12, 16, 26, and 30.

The 29-item MENQOL questionnaire assesses the presence and degree of bother associated with menopausal symptoms over the past week. 44 Participants indicated whether or not they experienced a particular symptom and rated its level of bother on a 7-point scale (range, 0-6, with higher scores indicating a higher level of bother). The 29 items assess 4 domains of symptoms and functioning: VMS (items 1-3), psychosocial (items 4-10), physical (items 11-26), and sexual (items 27-29) domains. Responses to single items were used to calculate 29 individual item scores. The 4 domain scores were calculated as a mean of converted single-item scores (range, 1-8, with higher scores indicating a higher level of bother), and the mean of the 4 domain scores yielded the MENQOL total score. Participants completed the questionnaire at baseline and weeks 4, 8, 12, 16, 26, and 30. The development and validation of PROMIS SD SF 8b and MENQOL and their use in VMS clinical trials have been described previously. 30 , 33 , 35 - 37 , 44 - 48

Safety was assessed throughout the trials by documenting adverse events (AEs), which were coded using the Medical Dictionary for Regulatory Authorities. Safety assessments also included laboratory assessments, transvaginal ultrasonography, and endometrial biopsies, among others. Consistent with FDA guidance, participants meeting the prespecified criteria for close liver observation, including those with increases in alanine aminotransferase and aspartate aminotransferase levels greater than 3 times the upper limit of normal, were followed up. These cases were assessed in a blinded fashion by an independent external liver safety monitoring board to identify potential drug-induced liver injury. 49 An independent data and safety monitoring board monitored the general safety of the participants in the trials.

A planned sample size of 370 participants per trial was derived based on simulations using the t test and Wilcoxon rank-sum test, considering an assumed drop-out rate of 10% in the first 3 months. Treatment effects and characteristics of the distributions were assumed from phase 2 SWITCH-1 results and a fixed level of correlation of 0.3 was anticipated between the end points. The trials were planned to achieve a power of at least 90% for all primary and key secondary end points considering the multiple testing strategy.

Statistical analysis was performed using SAS (release 9.4; SAS Institute Inc) and ValidR (R version 3.5.2 50 ; Mango Solutions Ltd). Efficacy analyses were performed on the full analysis set where all randomized participants were included. Participants in the full analysis set were analyzed according to the randomized intervention (intention to treat). Safety analyses were performed on the safety analysis set that included all participants who received at least 1 dose of study intervention. Participants in the safety analysis set were analyzed according to the intervention they received.

The primary and key secondary end points were analyzed for each trial using a mixed model with repeated measures on the change from baseline at different weeks. Fixed effects in the model included baseline values of the respective end point, treatment, region, and week, as well as the interaction terms baseline by week and treatment by week. Prior to modeling, missing and collected data that occurred in the presence of intercurrent events were handled in alignment with the predefined estimand, as provided in eTable 4 in Supplement 3 . 51 A multiplicity adjustment strategy for each trial was defined using the graphical approach, 52 controlling the overall type I error rate at a 1-sided α level of .025 for confirmatory statistical superiority testing. All presented P values are from 1-sided statistical testing. Full details of the statistical analysis, including assessed model diagnostics and sensitivity analysis, are provided in the statistical analysis plan in Supplement 2 .

OASIS 1 was conducted between August 27, 2021, and November 27, 2023, across 77 sites in the US, Europe, and Israel. OASIS 2 was conducted between October 29, 2021, and October 10, 2023, across 77 sites in Canada, the US, and Europe (eTables 1 and 2 in Supplement 3 ). The participant disposition is summarized in Figure 1 . Baseline demographics were generally balanced between treatment groups in both trials, with slight imbalances in smoking history in opposite directions in the 2 studies ( Table 1 ).

At baseline, OASIS 1 participants in the elinzanetant and placebo groups experienced a mean (SD) of 13.4 (6.6) and 14.3 (13.9) VMS per 24 hours, respectively; similar numbers of VMS, 14.7 (11.1) and 16.2 (11.2), respectively, were observed in OASIS 2. In OASIS 1, mean (SD) descriptive changes from baseline to week 4 were −7.5 (5.8) and −4.4 (6.7) in the elinzanetant and placebo groups, respectively, corresponding to a mean (SD) percentage change from baseline of −55.9% (34.1%) and −31.4% (33.8%). At week 12, these changes were −8.7 (6.7) and −5.5 (10.2), respectively, with mean (SD) percentage changes of −65.2% (35.3%) and −42.2% (43.3%). In OASIS 2, similar trends were seen, with mean (SD) changes from baseline to week 4 of −8.6 (9.2) and −6.1 (8.9), corresponding to mean (SD) percentage changes of −57.9% (34.7%) and −35.7% (37.4%). Mean (SD) changes to week 12 were −10.0 (10.3) and −7.2 (8.5) and mean (SD) percentage changes were −67.0% (34.9%) and −45.9% (38.1%), respectively ( Figure 2 ; eFigures 1 and 2, eTables 5 and 6 in Supplement 3 ).

At baseline, OASIS 1 participants in the elinzanetant and placebo groups experienced a mean (SD) of 2.6 (0.2) and 2.5 (0.2) in VMS severity, respectively; in OASIS 2, baseline severity was 2.5 (0.2) and 2.5 (0.2), respectively. Reductions were also seen for VMS severity from baseline to weeks 4 and 12 in both studies based on descriptive statistics, with greater mean numerical reductions in the elinzanetant groups than the placebo groups ( Figure 2 ; eFigure 3 and eTable 7 in Supplement 3 ).

In both trials, reductions in VMS frequency and severity from baseline to weeks 4 and 12 were statistically significantly greater for elinzanetant vs placebo. Least square (LS) mean changes in daily VMS frequency vs placebo from baseline to week 4 were −3.3 (95% CI, −4.5 to −2.1; P  < .001) for OASIS 1 and −3.0 (95% CI, −4.4 to −1.7; P  < .001) for OASIS 2. At week 12, LS mean changes from baseline vs placebo were −3.2 (95% CI, −4.8 to −1.6; P  < .001) for OASIS 1 and −3.2 (95% CI, −4.6 to −1.9; P  < .001) for OASIS 2. Reductions in daily VMS frequency vs placebo from baseline to week 1 were statistically significant in both trials (OASIS 1: −2.5 [95% CI, −3.4 to −1.6], P  < .001; OASIS 2: −1.7 [95% CI, −2.7 to −0.6], P  = .001). LS mean changes in daily VMS severity vs placebo from baseline to week 4 were −0.3 (95% CI, −0.4 to −0.2; P  < .001) in OASIS 1 and −0.2 (95% CI, −0.3 to −0.1; P  < .001) in OASIS 2. At week 12, changes from baseline vs placebo were −0.4 (95% CI, −0.5 to −0.3; P  < .001) in OASIS 1 and −0.3 (95% CI, −0.4 to −0.1; P  < .001) in OASIS 2.

At week 4, 62.8% and 62.2% of participants in the elinzanetant group achieved at least a 50% reduction in VMS frequency in OASIS 1 and 2, respectively, compared with 29.2% and 32.3% in the placebo group. At week 12, 71.4% and 74.7% in the elinzanetant group achieved at least a 50% reduction in OASIS 1 and 2, respectively, compared with 42.0% and 48.3% in the placebo group.

Participants reported improvements from baseline in sleep disturbances (assessed by the PROMIS SD SF 8b total T scores) across both treatment groups ( Figure 3 ; eFigure 4 and eTable 8 in Supplement 3 ); differences were statistically significant at week 12 vs placebo in both trials (difference in LS means for OASIS 1: −5.6 [95% CI, −7.2 to −4.0], P  < .001; OASIS 2: −4.3 [95% CI, −5.8 to −2.9], P  < .001). Improvements were also observed with PROMIS total raw scores, based on descriptive analyses (eTable 9 in Supplement 3 ).

Participants reported improvements in menopause-related quality of life (assessed by the MENQOL total score) from baseline ( Figure 3 ; eFigure 5 and eTable 10 in Supplement 3 ). Improvements seen in the elinzanetant group from baseline to week 12 were statistically significant vs placebo (difference in LS means for OASIS 1: −0.4 [95% CI, −0.6 to −0.2], P  < .001; OASIS 2: −0.3 [95% CI, −0.5 to −0.1], P  = .0059).

Based on descriptive analyses, the largest changes from baseline were observed in the VMS domain. At week 12, the mean (SD) changes from baseline in the VMS domain score in the elinzanetant group were −2.86 (2.11) in OASIS 1 and −2.70 (1.99) in OASIS 2. In the placebo group, the mean changes from baseline were −1.50 (1.80) and −1.64 (1.93), respectively, at week 12.

Reductions in the frequency and severity of VMS, PROMIS SD SF 8b total T score, and MENQOL total score in the elinzanetant group were maintained throughout the 26-week treatment period. Further improvements in these measures were also observed in the group that switched from placebo to elinzanetant after week 12 (eTables 5-10 and eFigures 2-5 in Supplement 3 ) based on descriptive analyses.

By week 26, more than 80% of participants had achieved at least a 50% reduction in VMS frequency in the elinzanetant group (81.6% and 81.5% in OASIS 1 and 2, respectively) and in those who switched to elinzanetant after week 12 (84.5% and 86.7% in OASIS 1 and 2, respectively).

In OASIS 1, treatment-emergent adverse events (TEAEs) were reported in 51.3% of participants in the elinzanetant group and 48.5% in the placebo group over the 12-week placebo-controlled treatment period ( Table 2 ; eTables 11 and 12 in Supplement 3 ). In OASIS 2, 44.3% and 38.2% of participants reported TEAEs in the elinzanetant and placebo groups, respectively. In both trials, most TEAEs were of mild or moderate intensity, and there were few serious AEs (eTables 11 and 12 in Supplement 3 ).

Across both trials, headache and fatigue (Medical Dictionary for Regulatory Activities preferred term) occurred more frequently in the elinzanetant groups during the 12-week, placebo-controlled treatment period (7.0%-9.0% vs 2.5%-2.6% for headache and 5.5%-7.0% vs 1.5% for fatigue) ( Table 2 ). Both fatigue and headache were reported less frequently by participants in the placebo group after switching to elinzanetant compared with those initially randomized to elinzanetant (2.2%-3.6% for headache and 0.6%-1.7% for fatigue during weeks 13-26) (eTable 13 in Supplement 3 ). Most events were of mild intensity, and none were severe. Based on a post hoc analysis, for fatigue, the highest reported daily relative frequency (up to 5%) was observed during the first weeks, which was then reduced to less than 3% after week 13. For headache, the highest daily relative frequency (up to 5.5%) was reported around week 7 to 8 and reduced to less than 2% toward the end of the treatment period.

There were no cases of liver enzyme elevations meeting criteria for liver injury as assessed by the liver safety monitoring board. There were no cases of endometrial hyperplasia or malignant neoplasm in either trial as assessed by 3 independent pathologists. There were no clinically relevant changes in vital signs or laboratory parameters throughout the study, and no new safety signals were observed throughout either trial (eTable 13 in Supplement 3 ).

OASIS 1 and 2 are the first phase 3 trials evaluating the efficacy and safety of elinzanetant—a nonhormonal, selective NK-1 and NK-3 receptor antagonist—in postmenopausal individuals with moderate to severe VMS. Results from both studies are consistent with regard to the primary and key secondary end points and in line with previous results of the phase 2 study SWITCH-1, 30 thereby demonstrating the reproducibility of and increasing confidence in these findings. In both OASIS 1 and 2 trials, elinzanetant achieved statistically significant reductions from baseline in VMS frequency and severity vs placebo as well as improvements in sleep disturbances and menopause-related quality of life.

A reduction of at least 2 moderate to severe VMS per day above placebo (14 per week) has been identified by the FDA as a clinically meaningful reduction on a group level. 53 , 54 Considering this, elinzanetant reached a clinically meaningful reduction in daily VMS frequency compared with placebo as early as week 1 in OASIS 1, and at weeks 4 and 12 in both trials. In addition, a reduction from baseline in VMS frequency of at least 50% has been described in the literature as a relevant improvement on an individual level. 55 Based on descriptive analyses, more than 70% of participants in the elinzanetant group achieved a response by week 12, and more than 80% of participants achieved a response by the end of treatment using this threshold. These results have clinically relevant implications because VMS often pose significant impacts on menopausal individual’s overall health, everyday activities, sleep, quality of life, and work productivity. 4 - 6 , 8 - 12

Nighttime VMS can affect sleep quantity and quality, but sleep disturbances experienced during menopause can occur independently of VMS. 56 Sleep disturbances can substantially impair quality of life during menopause 1 , 3 , 4 and additionally have implications for women’s physical health as they age. 13 Individuals experiencing menopausal sleep disturbances often use sleep medications such as benzodiazepines, 57 which can be associated with increased risk of falls as well as dependence or overuse. 58 , 59 Studies have reported mixed results regarding the efficacy of hormone therapy in addressing sleep problems among those with and without VMS. 60 - 62 A small, double-blind, randomized crossover study in healthy male volunteers showed the involvement of SP (NK-1 receptor ligand) in sleep. After intravenous infusion of SP, both polysomnography and subjective sleep ratings indicated a significant decrease in sleep quantity and quality. 63 Improvements in wakefulness after sleep onset in primary insomnia have been shown for an NK-1–specific receptor antagonist. 29 However, a role for SP/NK-1 receptors in sleep disturbance associated with menopause has not previously been established. Elinzanetant demonstrated significant reductions in sleep disturbance and improvements in sleep quality in the SWITCH-1 study. 30 In OASIS 1 and 2, at baseline, participants were on average experiencing moderate sleep disturbances according to the PROMIS SD SF 8b total T score classification established in a reference population (eTable 8 in Supplement 3 ). 42 Following treatment with elinzanetant, mean scores were reduced to the normal range according to the cut points published for the reference population; while scores in the placebo group only fell within the mild sleep disturbance range. 42 These data confirm that elinzanetant, as a selective dual NK-1,3 receptor antagonist, improves sleep disturbances in menopausal women.

A decrease in MENQOL total score of at least 0.9 from baseline has been identified as a clinically relevant response to treatment. 34 On average, both elinzanetant, 120 mg, and placebo demonstrated a clinically relevant improvement in menopause-related quality of life from baseline to week 12 using this threshold. 37 However, elinzanetant demonstrated statistically significant greater improvements in menopause-related quality of life compared with placebo. Further improvements up to 26 weeks were also observed in the participants who switched to elinzanetant after week 12. Consistent with reported findings from SWITCH-1, the MENQOL total score in OASIS 1 and 2 is considered to be predominantly driven by the VMS domain score. This is consistent with the impact of elinzanetant on reducing VMS frequency and severity. The improvements in the VMS domain are suggested to have a beneficial effect on other aspects of quality of life over time.

Elinzanetant maintained a favorable safety profile in line with its phase 2b clinical trial. 30 The most frequently reported adverse events in the elinzanetant group during the 12-week placebo-controlled period were headache and fatigue. In participants who switched from placebo to elinzanetant after week 12, few cases of headache and fatigue were reported. No incidences of endometrial hyperplasia or malignant neoplasm were seen in either trial.

Increases in liver enzymes were closely monitored in the OASIS trials due to previous concerns of elevations in liver transaminases with NK-3 receptor antagonists. 26 , 27 , 64 No incidences of liver toxicity were observed with elinzanetant in the OASIS 1 and 2 trials. Overall, the safety profile of elinzanetant was favorable when compared with placebo in the OASIS 1 and 2 studies over 12 weeks and with extended use up to 26 weeks. Additional safety data will be available from the 52-week, placebo-controlled OASIS 3 study ( NCT05030584 ).

The OASIS 1 and 2 trials had a number of strengths. Both were randomized, double-blind, multinational, and placebo-controlled trials specifically designed to evaluate the efficacy of elinzanetant for the treatment of VMS, while also assessing the effect of elinzanetant on sleep disturbances and menopause-related quality of life. Results demonstrated high reproducibility, with treatment effects consistent across all primary and key secondary end points over time.

The OASIS 1 and 2 trials had some limitations. First, all data for the primary and key secondary end points were collected electronically using diaries or questionnaires (patient-reported outcomes). Such measures are appropriate for measuring end points such as VMS, sleep disturbance, and menopause-related quality of life, which are highly influenced by participant perception. In addition, these can be associated with considerable participant burden. The subjective nature of patient-reported outcomes, together with regression to the mean symptom burden over time and the caring effect experienced during the study, may help explain the placebo response seen in these and other similar clinical trials. 65 , 66 Of note, this is an aspect common to studies investigating VMS treatments, with a recent meta-analysis demonstrating VMS placebo responses typically ranging from 34% to 67%. 67

Second, the OASIS 1 and 2 trials included only individuals with VMS who were naturally or surgically postmenopausal. Similar to other VMS trials, the participants were primarily White, with 12% to 19% Black or African American and less than 10% Hispanic or Latino. Significant unmet needs remain for other populations of individuals experiencing VMS, such as perimenopausal individuals and those experiencing VMS due to endocrine therapy for breast cancer. OASIS 4 ( NCT05587296 ) will assess the efficacy and safety of elinzanetant among individuals with or at high risk of breast cancer receiving tamoxifen or aromatase inhibitors.

Third, the participants in the OASIS 1 and 2 trials were not required to have sleep disturbances to be included. Although significant reductions in sleep disturbances were seen compared with placebo, further characterization is needed to assess the effect of elinzanetant in populations with sleep disturbances associated with menopause.

OASIS 1 and 2 were 2 similar pivotal phase 3 trials performed across different sites and countries that separately demonstrated the efficacy of elinzanetant for the treatment of VMS associated with menopause. Elinzanetant demonstrated a rapid improvement in VMS frequency at 1 week and robust improvements in VMS severity, sleep disturbances, and menopause-related quality of life, and has a favorable safety profile. Elinzanetant has the potential to provide a well-tolerated and efficacious nonhormonal treatment option to address the unmet health needs of many menopausal individuals with moderate to severe VMS.

Accepted for Publication: July 8, 2024.

Published Online: August 22, 2024. doi:10.1001/jama.2024.14618

Corresponding Author: JoAnn V. Pinkerton, MD, MSCP, Northridge Midlife Health, University of Virginia Health, Box 801104, Charlottesville, VA 22908 ( [email protected] ).

Author Contributions: Dr Zuurman and Ms Haseli Mashhadi had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Simon, Joffe, Panay, Caetano, Haberland, Mellinger, Parke, Seitz, Zuurman.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Pinkerton, Maki, Panay, Soares, Caetano, Krahn, Mellinger, Parke, Zuurman.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Panay, Haseli Mashhadi, Krahn, Mellinger, Parke, Zuurman.

Administrative, technical, or material support: Pinkerton, Caetano, Haberland.

Supervision: Simon, Nappi, Panay, Soares, Caetano, Haberland, Seitz, Zuurman.

Conflict of Interest Disclosures: Dr Pinkerton reported grants from Bayer Pharmaceuticals to University of Virginia during the conduct of the study and consulting fees from Bayer Pharmaceutical to University of Virginia and independent contract work for Merck for chapters on abnormal bleeding and menopause. Dr Simon reported grants from Bayer Healthcare, AbbVie, Daré Bioscience, Mylan, and Myovant/Sumitomo and personal fees from Astellas Pharma, Ascend Therapeutics, California Institute of Integral Studies, Femasys, Khyra, Madorra, Mayne Pharma, Pfizer, Pharmavite, Scynexis Inc, Vella Bioscience, and Bayer; and stock from Sermonix Pharmaceuticals outside the submitted work. Dr Joffe reported personal fees from Bayer, Merck, and Hello Therapeutics and grants from National Institutes of Health and Merck outside the submitted work; Dr Joffe’s spouse is an employee of Arsenal Biosciences and has equity in Merck. Dr Maki reported personal fees from Bayer Consumer Care during the conduct of the study and personal fees from Astellas and Pfizer and equity from Midi Health, Alloy, and Estrigenix outside the submitted work, as well as serving as a trustee of the International Menopause Society. Dr Nappi reported personal fees from Abbott, Astellas, Bayer Healthcare, Besins Healthcare, Exeltis, Fidia, Merck & Co, Novo Nordisk, Organon, Shionogi, Theramex, and Viatris; grants from Gedeon Richter; and nonfinancial support from HRA Pharma outside the submitted work. Dr Panay reported personal fees from Bayer during the conduct of the study and personal fees from Abbott, Astellas, Besins, Gedeon Richter, Lawley, Theramex, and Viatris outside the submitted work. Dr Soares reported grants from Ontario Brain Institute, Eisai, Clairvoyant Therapeutics, and Diamond Therapeutics and personal fees from CAN-BIND Solutions, Otsuka, and Ontario Health outside the submitted work. Dr Thurston reported personal fees from Bayer during the conduct of the study and personal fees from Astellas, Hello Therapeutics, Happify Health (relationship has ended), and Vira Health (relationship has ended) outside the submitted work. No other disclosures were reported.

Funding/Support: The OASIS 1 and 2 trials were sponsored by Bayer.

Role of the Funder/Sponsor: Bayer had a 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 Presentations: Data reported in this article have been previously presented as posters at the American College of Obstetricians and Gynecologists Annual Meeting; May 17-19, 2024; San Francisco, California.

Data Sharing Statement: See Supplement 4 .

Additional Contributions: We acknowledge and thank the individuals who participated in the OASIS 1 and 2 trials and the investigators, including high recruiters in OASIS 1: Ramin Farsad, Diagnamics Inc; Ondrej Mika, GYN-Mika sro; Elliot Shin, Jubilee Clinical Research Inc; Micah Harris, MomDoc Women for Women; Leopold Rotter, Gynekologie MEDA sro; Mona Fakih, Revive Research Institute; Levente Rubliczky, Rub-Int Noi Egeszsegcentrum; Rossella Nappi, IRCCS Policlinico S Matteo; Carrie Swartz, Bosque Women’s Care; and Karl Tamussino, Univ Frauenklinik Graz, and in OASIS 2: Janusz Tomaszewski, Gabinet Gin J Tomaszewski; Adrian Marimon, Sweet Hope Research Specialty; Allan Dinnerstein, Helix Biomedics; Dagmara Makowska-Mainka, Clinical Medical Research; Andrea Heweker, Praxis Fuer Gynaekologie; Robert Smith, Suncoast Clinical Research; Ethel Bellavance, Diex Victoriaville; Sophia Rahman, ClinRx Research LLC; Michael Livingston, Metro Jackson OBGYN/SKYCRNG; Patrik Horvath, GYNARIN sro; Saskia Kerschischnik, emovis GmbH; Michael Moore, Advanced Women’s Health Institute; and Jeffrey Wayne, Clinical Trials Research. They all received compensation. Medical writing assistance was provided by Emma Case, MSci, of Highfield, Oxford, UK with sponsorship from Bayer.

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