What statistical method is used to analyze time-to-event outcomes in clinical trials?

What statistical method is used to analyze time-to-event outcomes in clinical trials?

• A. Kaplan-Meier analysis
• B. Chi-square test
• C. Regression analysis
• D. Logistic regression

Answer: (A)

Kaplan-Meier analysis is the preferred statistical method for analyzing time-to-event outcomes, commonly referred to as survival analysis, in clinical trials. This method allows researchers to estimate the survival function from lifetime data, providing insights into the time until a specified event occurs, such as death or disease progression. The Kaplan-Meier estimator generates a survival curve that represents the probability of surviving beyond a certain time point, accommodating censored data, which occurs when patients leave the study or have not experienced the event by the end of the observation period. This capability makes it especially useful in clinical settings where follow-up may be incomplete for some participants. Additionally, Kaplan-Meier analysis facilitates the comparison between different treatment groups through log-rank tests, thereby enhancing its applicability in clinical trials aimed at assessing the effectiveness of new therapies over time.

Kaplan-Meier analysis is the preferred statistical method for analyzing time-to-event outcomes, commonly referred to as survival analysis, in clinical trials. This method allows researchers to estimate the survival function from lifetime data, providing insights into the time until a specified event occurs, such as death or disease progression.

The Kaplan-Meier estimator generates a survival curve that represents the probability of surviving beyond a certain time point, accommodating censored data, which occurs when patients leave the study or have not experienced the event by the end of the observation period. This capability makes it especially useful in clinical settings where follow-up may be incomplete for some participants.

Additionally, Kaplan-Meier analysis facilitates the comparison between different treatment groups through log-rank tests, thereby enhancing its applicability in clinical trials aimed at assessing the effectiveness of new therapies over time.