Bayesian Modeling and Inference for Data with Informative Treatment Switching or Dropout

• Activity Number: 379
• Type: Invited
• Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Health Policy Statistics
• Author(s): Ming-Hui Chen*+ and Qingxia Chen and David Ohlssen and Joseph G. Ibrahim
• Companies: University of Connecticut and Vanderbilt University and Novartis and The University of North Carolina
• Keywords: Intermittent missingness ; Markov chain Monte Carlo ; Missing at random ; Multivariate logistic regression ; Multivariate mixed-effects model ; Non-ignorable

Abstract

In randomized clinical trials, it is common that patients may stop taking their assigned treatments and then start the standard treatment or completely dropout from the study. In addition, patients may miss scheduled visits even during the study, leading to intermittent missingness. In this paper, we develop a novel Bayesian method for jointly modeling longitudinal treatment measurements under various dropout scenarios. Specifically, we propose a multivariate normal mixed-effects model for repeated measurements from the assigned treatments and the standard treatment, a multivariate logistic regression model for those stopping the assigned treatments, logistic regression models for those starting a standard treatment off protocol, and a conditional multivariate logistic regression model for completely withdrawing from the study. We assume that withdrawing from the study is non-ignorable but intermittent missingness is assumed to be at random. Various properties of the proposed model are examined. An efficient Markov chain Monte Carlo sampling algorithm is developed. A real data set from a clinical trial is analyzed in detail via the proposed method.