Alternative Analysis Methods for Time to Event Endpoints under Non-proportional Hazards: A Comparative Analysis

TL;DR

This study compares various statistical tests for time-to-event data under non-proportional hazards, highlighting the robustness of the MaxCombo test and emphasizing the importance of multiple effect measures for comprehensive analysis.

Contribution

It provides a comparative evaluation of multiple testing methods under non-PH scenarios, recommending the MaxCombo test for its robustness when prior pattern knowledge is unavailable.

Findings

All tests control type I error well.

No single test is most powerful across all scenarios.

MaxCombo test is relatively robust across different patterns.

Abstract

The log-rank test is most powerful under proportional hazards (PH). In practice, non-PH patterns are often observed in clinical trials, such as in immuno-oncology; therefore, alternative methods are needed to restore the efficiency of statistical testing. Three categories of testing methods were evaluated, including weighted log-rank tests, Kaplan-Meier curve-based tests (including weighted Kaplan-Meier and Restricted Mean Survival Time, RMST), and combination tests (including Breslow test, Lee's combo test, and MaxCombo test). Nine scenarios representing the PH and various non-PH patterns were simulated. The power, type I error, and effect estimates of each method were compared. In general, all tests control type I error well. There is not a single most powerful test across all scenarios. In the absence of prior knowledge regarding the PH or non-PH patterns, the MaxCombo test is relatively robust across patterns. Since the treatment effect changes overtime under non-PH, the overall profile of the treatment effect may not be represented comprehensively based on a single measure. Thus, multiple measures of the treatment effect should be pre-specified as sensitivity analyses to evaluate the totality of the data.