Two-stage Adaptive Biomarker-Targeted Clinical Trial Design: Non-Parametric Bayesian and MLE Approaches

Biomarker targeted two-stage adaptive design is used increasingly in early-stage clinical trials in a variety of therapeutic areas including oncology, where the sample size of the trial is re-estimated based on the first stage data. In such trials often the sample size is moderate, and so incorporating prior information and using robust methods are desirable. In this article, to improve upon existing methods using parametric normal models, we propose a nonparametric Bayesian approach for designing such adaptive trials in a phase IIb/IIIa setting comparing a treatment vs. a control. Extensive simulation studies are conducted to evaluate the performance of the proposed method and compare it with the existing normal parametric model. Our results indicate that with good prior information, more reasonable and robust inference than with existing parametric methods can be obtained.