期刊
STATISTICS IN MEDICINE
卷 40, 期 20, 页码 4457-4472出版社
WILEY
DOI: 10.1002/sim.9041
关键词
Bayesian additive regression tree; Bayesian design; complier average causal effect; noncompliance; principal stratification; variable selection
In this study, a Bayesian approach was proposed to monitor noncompliance in clinical trials, using principal stratification framework and Bayesian additive regression trees. The design showed excellent performance in simulation studies when dealing with noncompliance issues.
Noncompliance issue is common in early phase clinical trials; and may lead to biased estimation of the intent-to-treat effect and incorrect conclusions for the clinical trial. In this work, we propose a Bayesian approach for sequentially monitoring the phase II randomized clinical trials that takes account for the noncompliance information. We adopt the principal stratification framework and propose to use Bayesian additive regression trees for selecting useful baseline covariates and estimating the complier average causal effect (CACE) for both efficacy and toxicity outcomes. The decision of early termination or not is then made adaptively based on the estimated CACE from the accumulated data. Simulation studies have confirmed the excellent performance of the proposed design in the presence of noncompliance.
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