Bayesian inference in time-varying additive hazards models with applications to disease mapping.

ABSTRACT: Environmental health and disease mapping studies are often concerned with the evaluation of the combined effect of various socio-demographic and behavioral factors, and environmental exposures on time-to-events of interest, such as death of individuals, organisms or plants. In such studies, estimation of the hazard function is often of interest. In addition to known explanatory variables, the hazard function maybe subject to spatial/geographical variations, such that proximally located regions may experience similar hazards than regions that are distantly located. A popular approach for handling this type of spatially-correlated time-to-event data is the Cox's Proportional Hazards (PH) regression model with spatial frailties. However, the PH assumption poses a major practical challenge, as

SUBMITTER: Chernoukhov A 

PROVIDER: S-EPMC6268206 | biostudies-literature | 2018 Aug-Sep

REPOSITORIES: biostudies-literature