Which measure is applicable for assessing survival data in clinical studies?

The Kaplan-Meier curve is an effective measure for assessing survival data in clinical studies because it provides a way to estimate the survival function from time-to-event data. It allows researchers to visualize the proportion of subjects surviving over time, taking into account right-censoring, which occurs when a patient leaves the study or is lost to follow-up before the event (such as death) occurs.

The Kaplan-Meier method is particularly useful in comparing survival rates between different groups, which can be illustrated by the curves. The log-rank test is often used in conjunction with the Kaplan-Meier curves to statistically compare the survival distributions of two or more groups.

When examining survival outcomes, other statistical tests such as the Mann-Whitney U test, logistic regression, or analysis of variance are not appropriate. The Mann-Whitney U test is designed for comparing differences between two independent groups on a continuous or ordinal variable, but it does not handle time-to-event data specifically. Logistic regression is typically used for binary outcome data rather than time-to-event analysis. Analysis of variance (ANOVA) is suited for comparing means among three or more groups in continuous data but is not applicable for survival duration measurements. Therefore, the Kaplan-Meier curve is the correct and applicable method