Abstract

With the emergence of novel therapies exhibiting distinct mechanisms of action compared to traditional treatments, departure from the proportional hazard (PH) assumption in clinical trials with a time-to-event end point is increasingly common. In these situations, the hazard ratio may not be a valid statistical measurement of treatment effect, and the log-rank test may no longer be the most powerful statistical test. The restricted mean survival time (RMST) is an alternative robust and clinically interpretable summary measure that does not rely on the PH assumption. We conduct extensive simulations to evaluate the performance and operating characteristics of the RMST-based inference and against the hazard ratio-based inference, under various scenarios and design parameter setups. The log-rank test is generally a powerful test when there is evident separation favoring 1 treatment arm at most of the time points across the Kaplan-Meier survival curves, but the performance of the RMST test is similar. Under non-PH scenarios where late separation of survival curves is observed, the RMST-based test has better performance than the log-rank test when the truncation time is reasonably close to the tail of the observed curves. Furthermore, when flat survival tail (or low event rate) in the experimental arm is expected, selecting the minimum of the maximum observed event time as the truncation timepoint for the RMST is not recommended. In addition, we recommend the inclusion of analysis based on the RMST curve over the truncation time in clinical settings where there is suspicion of substantial departure from the PH assumption.

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