A two-step approach for analyzing time to event data under non-proportional hazards
Abstract
The log-rank test and the Cox proportional hazards model are commonly used to compare time-to-event data in clinical trials, as they are most powerful under proportional hazards. But there is a loss of power if this assumption is violated, which is the case for some new oncology drugs like immunotherapies. We consider a two-stage test procedure, in which the weighting of the log-rank test statistic depends on a pre-test of the proportional hazards assumption. I.e., depending on the pre-test either the log-rank or an alternative test is used to compare the survival probabilities. We show that if naively implemented this can lead to a substantial inflation of the type-I error rate. To address this, we embed the two-stage test in a permutation test framework to keep the nominal level alpha. We compare the operating characteristics of the two-stage test with the log-rank test and other tests by clinical trial simulations.
Cite
@article{arxiv.2402.08336,
title = {A two-step approach for analyzing time to event data under non-proportional hazards},
author = {Jonas Brugger and Tim Friede and Florian Klinglmüller and Martin Posch and Robin Ristl and Franz König},
journal= {arXiv preprint arXiv:2402.08336},
year = {2024}
}