Bayesian phase II clinical trial design with noncompliance

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...

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Bibliographic Details
Published in:Statistics in medicine 2021-09, Vol.40 (20), p.4457-4472
Main Authors: Ren, Tingyang, Shen, Weining, Zhang, Liwen, Zhao, Haibing
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian additive regression tree
Bayesian design
Causality
Clinical trials
Clinical Trials, Phase II as Topic
complier average causal effect
Computer Simulation
Humans
Noncompliance
Patient Compliance
principal stratification
variable selection
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Summary: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.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.9041