title: Biomarker-based Bayesian randomized phase II clinical trial design to identify a sensitive patient subpopulation.
abstract: The benefits and challenges of incorporating biomarkers into the development of anticancer agents have been increasingly discussed. In many cases, a sensitive subpopulation of patients is determined based on preclinical data and/or by retrospectively analyzing clinical trial data. 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. Given preliminary but promising efficacy data observed in a phase I study, it may be worth designing a phase II clinical trial that aims to identify a sensitive subpopulation. In order to achieve this goal, 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. The two methods basically estimate Bayesian posterior probabilities of PFS hazard ratios in biomarker subgroups. Extensive simulation studies evaluate these methods' operating characteristics, including the correct identification probabilities of the desired subpopulation under a wide range of clinical scenarios. We also examine the impact of subgroup population proportions on the methods' operating characteristics. 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.
authors.affiliation: Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan.
authors.forename: Satoshi
authors.initials: S
authors.lastname: Morita
authors.forename: Hideharu
authors.initials: H
authors.lastname: Yamamoto
authors.forename: Yasuo
authors.initials: Y
authors.lastname: Sugitani
mesh_terms: D061067:Antibodies, Monoclonal, Humanized / Q000235:genetics; D000970:Antineoplastic Agents; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics; D017322:Clinical Trials, Phase II as Topic / Q000379:methods*; D003198:Computer Simulation; D015195:Drug Design; D015340:Epidemiologic Research Design; D005260:Female; D005819:Genetic Markers; D006801:Humans; D008390:Markov Chains; D058990:Molecular Targeted Therapy / Q000379:methods*; D009010:Monte Carlo Method; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012044:Regression Analysis; D016019:Survival Analysis; D000068878:Trastuzumab
chemical_list: D061067:Antibodies, Monoclonal, Humanized; D000970:Antineoplastic Agents; D005819:Genetic Markers; D000068878:Trastuzumab
keywords: Bayesian statistics; biomarker; molecular-targeted agent; randomized phase II trial; time-to-event data