Principal Stratification Approach for Bayesian Sequential Monitoring Design to Address Noncompliance in Clinical Trials

• Activity Number: 55 - Recent Evaluations of Methods for Handling Noncompliance/Dropouts in Clinical Trials for Better Guidance Driven Application
• Type: Invited
• Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Weining Shen*
• Companies: University of California at Irvine
• Keywords: Estimand; Noncompliance; Bayesian design; Principle stratification; Causal effect; Continuous monitoring

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. We discuss a motivating smoking cessation example of placebo-controlled randomized phase II clinical trial that aims to evaluate the toxicity and efficacy of a new agent for the treatment of nicotine withdrawal symptoms. Numerical results and sensitivity analysis confirm that the proposed method has desirable operating characteristics and addresses the issue of noncompliance.