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.
Original language | English (US) |
---|---|
Pages (from-to) | 2104-2115 |
Number of pages | 12 |
Journal | Statistics in Medicine |
Volume | 34 |
Issue number | 13 |
DOIs | |
State | Published - Jun 15 2015 |
Keywords
- Bayesian design
- Causal effect
- Continuous monitoring
- Noncompliance
- Principal stratification
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability
MD Anderson CCSG core facilities
- Biostatistics Resource Group
- Clinical Trials Office