title: Bayesian sequential monitoring design for two-arm randomized clinical trials with noncompliance.
abstract: 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.
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, U.S.A.
authors.forename: Weining
authors.initials: W
authors.lastname: Shen
authors.forename: Jing
authors.initials: J
authors.lastname: Ning
authors.forename: Ying
authors.initials: Y
authors.lastname: Yuan
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D003198:Computer Simulation; D057239:Early Termination of Clinical Trials / Q000592:standards / Q000706:statistics & numerical data; D023381:Endpoint Determination; D006801:Humans; D010349:Patient Compliance; D016032:Randomized Controlled Trials as Topic / Q000592:standards* / Q000706:statistics & numerical data; D012107:Research Design; D016540:Smoking Cessation / Q000379:methods / Q000706:statistics & numerical data; D061485:Tobacco Use Cessation Devices / Q000009:adverse effects / Q000592:standards / Q000706:statistics & numerical data
keywords: Bayesian design; causal effect; continuous monitoring; noncompliance; principal stratification