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Bayesian sequential monitoring design for two‐arm randomized clinical trials with noncompliance
Author(s) -
Shen Weining,
Ning Jing,
Yuan Ying
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6474
Subject(s) - bayesian probability , covariate , computer science , interim , clinical trial , interim analysis , causal inference , sequential analysis , data monitoring committee , posterior probability , machine learning , econometrics , artificial intelligence , statistics , medicine , mathematics , archaeology , pathology , history
In early‐phase clinical trials, interim monitoring is commonly conducted based on the estimated intent‐to‐treat effect, which is subject to bias in the presence of noncompliance. To address this issue, we propose a Bayesian sequential monitoring trial design based on the estimation of the causal effect using a principal stratification approach. The proposed design simultaneously considers efficacy and toxicity outcomes and utilizes covariates to predict a patient's potential compliance behavior and identify the causal effects. Based on accumulating data, we continuously update the posterior estimates of the causal treatment effects and adaptively make the go/no‐go decision for the trial. Numerical results show that the proposed method has desirable operating characteristics and addresses the issue of noncompliance. Copyright © 2015 John Wiley & Sons, Ltd.