Bayesian Regression Analysis of Bivariate Group-Tested Current Status Data with Spatial Effects

Thomas M. Menino Convention & Exhibition Center 

Group testing has been a cost-effective strategy for screening rare diseases on large-scale populations. The strategy works by combining the specimens of blood or urine from multiple subjects and testing the pooled specimens instead of individual's. When a disease onset time is of interest, such group testing study design produces group-tested current status data, in which neither individual disease time nor the individual disease status is available, and only the group disease status is available. Our project studies joint analysis of two disease onset times simultaneously, for each of which only group-tested current status data are available. A new frailty model is proposed to incorporate both the dependence between correlated disease onset times and the spatial dependence among subjects sharing the same clinic. A fully Bayesian estimation approach is developed based on a data augmentation that lead to a complete data likelihood in an appealing form. The proposed Gibbs sampler is computationally efficient since all the latent variables and parameters are sampled from some well recognized full conditional distributions. Our method is evaluated by a simulation study and illustrate

Bivariate group-tested current status data

Frailty model

Misspecification

Gibbs sampler 

Section on Bayesian Statistical Science