• Yunqi Bu and Xiao-Hua Zhou.
• a Department of Biostatistics , University of Washington , Seattle , Washington , USA.
• J Biopharm Stat. 2016 Jan 1; 26 (6): 1118-1124.

AbstractPersonalized medicine is an area of growing attention in medical research and practice. A market-ready companion diagnostic test (CDx) is used in personalized medicine for identifying the best treatment for an individual patient. Unfortunately, development of CDx may lag behind the development of the drug, and consequently we use a clinical trial assay (CTA) to enroll patients into the drug pivotal clinical trial instead. Thus, when CDx becomes available, a bridging study will be required to assess the drug efficacy in the CDx intended use (CDx IU) population. Due to missingness of the CDx results that could be associated with randomization, one challenge we face in a bridging study is covariate imbalance between treatment arms for the subpopulation with both positive CDx and CTA. In this paper, we evaluate the performance of two methods in bridging studies under a causal inference framework. Particularly, we aim to use the propensity score method with doubly robust estimation and optimal matching to address the challenge. We extend under a current framework on drug efficacy estimation in the CDx IU population, using data from both the bridging study and the CTA drug pivotal clinical trial. Both approaches are discussed in the context of a randomized bridging study, and a targeted design clinical trial with simulations, followed by analyzing simulated data that mimic a real ongoing clinic trial.

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