Semiparametric Bayesian Joint Modeling of Clustered Binary and Continuous Outcomes with Informative Cluster Size

• Activity Number: 488
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Risk Analysis
• Author(s): Beom Seuk Hwang*+ and Michael L. Pennell
• Companies: The Ohio State University and The Ohio State University
• Keywords: Benchmark dose ; Developmental toxicology study ; Joint modeling ; Kernel stick breaking process ; Nonparametric Bayes ; Risk assessment

Abstract

Many authors have shown that joint modeling outcomes can effectively improve statistical power in studies involving clustered data (e.g., pups within litters, patients within hospitals). Most methods for joint modeling assume standard parametric response distributions. However, in developmental toxicity studies, it is possible that response distributions vary in location and shape with dose, which may not be easily captured by parametric models. Motivated by applications in developmental toxicity, we propose a semiparametric Bayesian joint model for clustered binary and continuous responses. In our model, a nested kernel stick-breaking process prior is assigned to the distribution of a random effect shared across outcomes, which allows simultaneous grouping of clusters and subjects within clusters, and flexible changes in distribution shape with dose shared across outcomes. We also account for informative cluster size by modeling the number of subjects in each cluster using a Poisson regression model with a cluster-specific random effect that is shared with the outcome variables. We apply our method to data from a developmental toxicity study of diethylhexyl phthalate.