Robust group sequential designs for trials with survival endpoints and delayed response

Abstract Randomized clinical trials in oncology typically utilize time‐to‐event endpoints such as progression‐free survival or overall survival as their primary efficacy endpoints, and the most commonly used statistical test to analyze these endpoints is the log‐rank test. The power of the log‐rank test depends on the behavior of the hazard ratio of the treatment arm to the control arm. Under the assumption of proportional hazards, the log‐rank test is asymptotically fully efficient. However, this proportionality assumption does not hold true if there is a delayed treatment effect. Cancer immunology has evolved over time and several cancer vaccines are available in the market for treating existing cancers. This includes sipuleucel‐T for metastatic hormone‐refractory prostate cancer, nivolumab for metastatic melanoma, and pembrolizumab for advanced nonsmall‐cell lung cancer. As cancer vaccines require some time to elicit an immune response, a delayed treatment effect is observed, resulting in a violation of the proportional hazards assumption. Thus, the traditional log‐rank test may not be optimal for testing immuno‐oncology drugs in randomized clinical trials. Moreover, the new immuno‐oncology compounds have been shown to be very effective in prolonging overall survival. Therefore, it is desirable to implement a group sequential design with the possibility of early stopping for overwhelming efficacy. In this paper, we investigate the max‐combo test, which utilizes the maximum of two weighted log‐rank statistics, as a robust alternative to the log‐rank test. The new test is implemented for two‐stage designs with possible early stopping at the interim analysis time point. Two classes of weights are investigated for the max‐combo test: the Fleming and Harrington (1981) weights and the Magirr and Burman (2019) modest weights.