Bayesian analysis of multivariate mortality data with large families

This paper presents a Bayesian method for the analysis of toxicological mul tivariate mortality data when the discrete mortality rate for each family o f subjects at a given time depends on familial random effects and the toxic ity level experienced by the family. Our aim is to model and analyse one se t of such multivariate mortality data with large family sizes: the potassiu m thiocyanate (KSCN) tainted fish tank data of O'Hara Hines. The model used is based on a discretized hazard with additional time-varying familial ran dom effects. A similar previous study (using sodium thiocyanate (NaSCN)) is used to construct a prior for the parameters in the current Study. A simul ation-based approach is used to compute posterior estimates of the model pa rameters and mortality rates and several other quantities of interest. Rece nt tools in Bayesian model diagnostics and variable subset selection have b een incorporated to verify important modelling assumptions regarding the ef fects of time and heterogeneity among the families on the mortality rate. F urther, Bayesian methods using predictive distributions are used for compar ing several plausible models.