Title: Biomarker-based Bayesian randomized phase II clinical trial design to identify a sensitive patient subpopulation
Authors: Satoshi Morita - Kyoto University Graduate School of Medicine (Japan) [presenting]
Abstract: The benefits and challenges of incorporating biomarkers into the development of anti-cancer agents have been increasingly discussed. Prospective exploration of sensitive subpopulations of patients may enable us to efficiently develop definitively effective treatments, resulting in accelerated drug development and a reduction in development costs. We consider the development of a new molecular-targeted treatment in cancer patients. We propose a Bayesian randomized phase II clinical trial design incorporating a biomarker that is measured on a graded scale. We compare two Bayesian methods, one based on subgroup analysis and the other on a regression model, to analyze a time-to-event endpoint such as progression-free survival (PFS) time. Extensive simulation studies evaluate these methods' operating characteristics under a wide range of clinical scenarios. Although both methods' performance depends on the distribution of treatment effect and the population proportions across patient subgroups, the regression-based method shows more favorable operating characteristics.