The New England Journal of Statistics in Data Science

We introduce the anytime-valid (AV) logrank test, a version of the logrank test that provides type-I error guarantees under optional stopping and optional continuation. The test is sequential without the need to specify a maximum sample size or stopping rule, and allows for cumulative meta-analysis with type-I error control. The method can be extended to define anytime-valid confidence intervals. The logrank test is an instance of the martingale tests based on E-variables that have been recently developed. We demonstrate type-I error guarantees for the test in a semiparametric setting of proportional hazards, show explicitly how to extend it to ties and confidence sequences and indicate further extensions to the full Cox regression model. Using a Gaussian approximation on the logrank statistic, we show that the AV logrank test (which itself is always exact) has a similar rejection region to O’Brien-Fleming α-spending but with the potential to achieve $100\% $ power by optional continuation. Although our approach to study design requires a larger sample size, the expected sample size is competitive by optional stopping.