Bayesian Modeling and Prediction of Patient Accrual in Multi-Regional Clinical Trials

• Activity Number: 318
• Type: Topic Contributed
• Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Xiaoxi Zhang*+ and Qi Long
• Companies: Pfizer Inc. and Emory University
• Keywords: Clinical Trial ; Multi-regional Trial ; Patient Accrual ; Nonhomogeneous Poisson Process ; Bayesian modeling

Abstract

With advances in medical research, effective treatments are becoming standard of care for many diseases. Consequently, when testing a new treatment, the sample size in a clinical trial is on the rise in order to demonstrate a moderate, yet clinically meaningful, improvement in therapeutic effect (compared to an active control), which often makes it impossible to enroll all patients from one region. In addition, regulatory requirements as well as other considerations may require trials to be conducted in multiple regions across the world. In multi-regional trials, the underlying overall and region-specic accrual rates often do not hold constant over time and different regions could have different start-up times, which combined with initial jump in accrual within each region often leads to a discontinuous overall accrual rate, and these issues associated with multi-regional trials have not been adequately investigated. In this paper, we clarify the implication of the multi-regional nature on modeling and prediction of accrual in clinical trials and investigate a Bayesian approach for accrual modeling and prediction, which models region-specificc accrual using a nonhomogeneous Poisson process (NHPP) and allows the underlying Poisson rate in each region to vary over time. The proposed approach can accommodate staggered start-up times and different start-up accrual rates across regions. Our numerical studies show that the proposed method improves precision of accrual prediction (i.e., tighter posterior predictive credible intervals) compared to an existing NHPP model that ignores region-specic data.