Speaker
Description
Time-to-event variables are among the most relevant primary efficacy endpoints in clinical trials, particularly in later phase oncology trials. When the proportional hazards assumption is expected to be severely violated, an alternative to the log-rank test is needed. Testing for differences in survival probabilities at a pre-defined time point offers one such option and has already been employed in some studies. However, in trials with stratified randomization, using the Kaplan-Meier estimate poses particular challenges: many common variance estimators are not evaluable or estimate a value of zero in strata with no events or in strata where all patients had the event, and the optimal stratum weighting strategy remains unclear.
Through a simulation study mimicking clinical trials with stratified randomization and various non-proportional hazards scenarios, we compared the stratified log-rank test and Kaplan-Meier-based Z-tests using different variance estimators and stratum weights, evaluating type-I error and power. While the log-rank test remained optimal under proportional hazards, some Z-tests provided robust performance across a broad range of scenarios and outperformed the log-rank test in situations with delayed treatment effects or crossing survival curves.
For stratified analyses under non-proportional hazards, we recommend a Kaplan-Meier-based Z-test with Borkowf's adjusted variance estimator and Mantel-Haenszel type weights, as inverse variance weights can lead to alpha-inflation. For non-stratified analyses, Greenwood-based or complementary log-log Z-tests are viable alternatives when zero variance can be excluded.
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