BAGS: A Bayesian Adaptive Group Sequential Trial Design With Subgroup-Specific Survival Comparisons.

ABSTRACT: A Bayesian group sequential design is proposed that performs survival comparisons within patient subgroups in randomized trials where treatment-subgroup interactions may be present. A latent subgroup membership variable is assumed to allow the design to adaptively combine homogeneous subgroups, or split heterogeneous subgroups, to improve the procedure's within-subgroup power. If a baseline covariate related to survival is available, the design may incorporate this information to improve subgroup identification while basing the comparative test on the average hazard ratio. General guidelines are provided for calibrating prior hyperparameters and design parameters to control the overall Type I error rate and optimize performance. Simulations show that the design is robust under a wide varie

SUBMITTER: Lin R 

PROVIDER: S-EPMC9109248 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature