Statistical Considerations for Evaluating Treatment Effect under Various Non-proportional Hazard Scenarios

TL;DR

This study systematically compares various statistical methods for analyzing time-to-event data under proportional and nonproportional hazard scenarios, using real oncology trial data and simulations to evaluate their performance.

Contribution

It introduces a comprehensive evaluation framework for multiple methods, including novel metrics for bias, and provides tailored recommendations for each hazard scenario.

Findings

MaxCombo and GGM outperform Log-rank in nonproportional hazards

New metrics effectively assess time-dependent bias in treatment effect estimates

Recommendations vary depending on the hazard scenario and method performance

Abstract

We conducted a systematic comparison of statistical methods used for the analysis of time-to-event outcomes under various proportional and nonproportional hazard (NPH) scenarios. Our study used data from recently published oncology trials to compare the Log-rank test, still by far the most widely used option, against some available alternatives, including the MaxCombo test, the Restricted Mean Survival Time Difference (dRMST) test, the Generalized Gamma Model (GGM) and the Generalized F Model (GFM). Power, type I error rate, and time-dependent bias with respect to the RMST difference, survival probability difference, and median survival time were used to evaluate and compare the performance of these methods. In addition to the real data, we simulated three hypothetical scenarios with crossing hazards chosen so that the early and late effects 'cancel out' and used them to evaluate the ability of the aforementioned methods to detect time-specific and overall treatment effects. We implemented novel metrics for assessing the time-dependent bias in treatment effect estimates to provide a more comprehensive evaluation in NPH scenarios. Recommendations under each NPH scenario are provided by examining the type I error rate, power, and time-dependent bias associated with each statistical approach.