Modeling of spatio-temporally clustered survival HIV/AIDS data in the presence of competing risks setting

Abstract

In some applications, clustered survival data are arranged spatio-temporally such as geographical regions over multiple time periods. Incorporating spatio-temporal variation in these data not only can improve the accuracy and efficiency of parameter estimation, but it also investigates spatial pattern of survivorship over the study period for identifying high-risk areas. Competing risks in survival data concern a situation where there is more than one cause of failure, but only the occurrence of the first one is observable. In this paper, we considered several Bayesian hierarchical survival models in the setting of competing risks for the spatio-temporally clustered HIV/AIDS data. An intrinsic conditional autoregressive (ICAR) distribution and a multivariate intrinsic conditional autoregressive (MICAR) distribution were employed to model random effect terms. The comparison between competing models was performed using the deviance information criterion and log pseudo-marginal likelihood. We illustrated the gains of final model through simulation study and application to the HIV/AIDS data.