The use of Bayesian hierarchical models for adaptive randomization in biomarker-driven phase II studies.
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.
a Department of Biostatistics and Computational Biology , Dana-Farber Cancer Institute , Boston , Massachusetts , USA.
William T
WT
Barry
Charles M
CM
Perou
P Kelly
PK
Marcom
Lisa A
LA
Carey
Joseph G
JG
Ibrahim
D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000378:metabolism*; D017322:Clinical Trials, Phase II as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000188:drug therapy* / Q000378:metabolism; D011237:Predictive Value of Tests; D011897:Random Allocation; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D018401:Sample Size; D016896:Treatment Outcome
D014408:Biomarkers, Tumor
Integral biomarkers; Phase II trials; Response adaptive