Work

The Use of Bayesian Hierarchical Models for Adaptive Randomization in Biomarker-Driven Phase II Studies

Year: 2014

Type: article

Abstract: The role of biomarkers has increased in cancer clinical trials such that novel designs are needed to efficiently answer questions of both drug effects and biomarker performance. We advocate Bayesian hierarchical models for response-adaptive randomized phase II studies integrating single or multiple biomarkers. Prior selection allows one to control a gradual and seamless transition from randomized-blocks to marker-enrichment during the trial. Adaptive randomization is an efficient design for evaluating treatment efficacy within biomarker subgroups, with less variable final sample sizes when compared to nested staged designs. Inference based on the Bayesian hierarchical model also has improved performance in identifying the sub-population where therapeutics are effective over independent analyses done within each biomarker subgroup.

Source: Journal of Biopharmaceutical Statistics

DOI: 10.1080/10543406.2014.919933

Authors: William T. Barry, Charles M. Perou, P. Kelly Marcom, Lisa A. Carey, Joseph G. Ibrahim

Institutions: Dana-Farber Cancer Institute, University of North Carolina at Chapel Hill, Duke Medical Center

Language: English

Topic: Statistical Methods in Clinical Trials

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: North Carolina Biotechnology Center, National Institutes of Health, National Cancer Institute, National Center for Research Resources