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arXiv · 2402.08336

A two-step approach for analyzing time to event data under non-proportional hazards

Jonas Brugger·Tim Friede·Florian Klinglmüller·Martin Posch·Robin Ristl·Franz König
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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.

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Jonas Brugger, Tim Friede, Florian Klinglmüller, Martin Posch, Robin Ristl, Franz König. 2024-02-13. A two-step approach for analyzing time to event data under non-proportional hazards. https://arxiv.org/abs/2402.08336

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