Testing the effectiveness of treatment for cancers for which the endpoint is survival using Bayesian subgroup analysis

We propose a basket trial design that tests the effectiveness of a new treatment for several types of cancers where the endpoint is the survival time. During the trial conduct, Bayesian subgroup analysis is conducted to classify the cancer types into different clusters according to both the survival time and the longitudinal biomarker measurements of the patient. Finally, we make Bayesian inferences to decide whether to stop recruiting patients for each cluster early and make conclusions about whether the treatment is effective for each cluster according to the estimated median survival time. The simulation study shows that our proposed method performs better than the independent approach and the Bayesian Hierarchical Modeling (BHM) method in most of the scenarios.