A Bayesian pick-the-winner design in a randomized phase II clinical trial
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Dung-Tsa Chen1, Po-Yu Huang2, Hui-Yi Lin3, Alberto A. Chiappori4, Dmitry I. Gabrilovich5, Eric B. Haura4 , Scott J. Antonia4 and Jhanelle E. Gray4
1 Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
2 Computational Intelligence Technology Center, Industrial Technology Research Institute, Taichung, Taiwan
3 Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, USA
4 Department of Thoracic Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
5 Translational Tumor Immunology, The Wistar Institute, Philadelphia, PA, USA
Dung-Tsa Chen, email:
Keywords: Bayesian posterior probability, Simon two-stage design, pick the winner design
Received: April 19, 2017 Accepted: April 24, 2017 Published: July 07, 2017
Purpose: Many phase II clinical trials evaluate unique experimental drugs/combinations through multi-arm design to expedite the screening process (early termination of ineffective drugs) and to identify the most effective drug (pick the winner) to warrant a phase III trial. Various statistical approaches have been developed for the pick-the-winner design but have been criticized for lack of objective comparison among the drug agents.
Methods: We developed a Bayesian pick-the-winner design by integrating a Bayesian posterior probability with Simon two-stage design in a randomized two-arm clinical trial. The Bayesian posterior probability, as the rule to pick the winner, is defined as probability of the response rate in one arm higher than in the other arm. The posterior probability aims to determine the winner when both arms pass the second stage of the Simon two-stage design.
Results: When both arms are competitive (i.e., both passing the second stage), the Bayesian posterior probability performs better to correctly identify the winner compared with the Fisher exact test in the simulation study. In comparison to a standard two-arm randomized design, the Bayesian pick-the-winner design has a higher power to determine a clear winner. In application to two studies, the approach is able to perform statistical comparison of two treatment arms and provides a winner probability (Bayesian posterior probability) to statistically justify the winning arm.
Conclusion: We developed an integrated design that utilizes Bayesian posterior probability, Simon two-stage design, and randomization into a unique setting. It gives objective comparisons between the arms to determine the winner.
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