| Popis: |
Many dose-response studies collect data on correlated outcomes. For example, in developmental toxicity studies, uterine weight and presence of malformed pups are measured on the same dam. Joint modeling can result in more efficient inferences than independent models for each outcome. Most methods for joint modeling assume standard parametric response distributions. However, in toxicity studies, it is possible that response distributions vary in location and shape with dose, which may not be easily captured by standard models. To address this issue, we propose a semiparametric Bayesian joint model for a binary and continuous response. In our model, a kernel stick-breaking process (KSBP) prior is assigned to the distribution of a random effect shared across outcomes, which allows flexible changes in distribution shape with dose shared across outcomes. The model also includes outcome-specific fixed effects to allow different location effects. Joint modeling is also common for clustered data in many biomedical studies (e.g., pups within litters, patients within hospitals). Many authors have shown that joint modeling can effectively improve statistical power in studies involving clustered data. We extend the model to allow for clustered data by combining the nested DP model with the KSBP model. A nested KSBP 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. In simulation studies, we found that the proposed model provides accurate estimates of toxicological risk when the data don't satisfy assumptions of standard parametric models. We apply our method to data from developmental toxicity studies. |
