Bayesian accelerated failure time model for correlated censored data with a normal mixture as an error distribution
Abstract
A Bayesian approach is proposed for an accelerated failure time model with interval-censored data. The model allows for structured correlated data by inclusion of a random effect part that might depend on covariates, as in a linear mixed model.
The error distribution is modelled as a normal mixture with an unknown number of components. Also, the means and variances of the components are not prespecified so as to accommodate most continuous distributions.
This results, among other things, in a nearly correct estimation of the shape of the hazard and survivor curves. A Markov chain Monte Carlo algorithm is described that samples from the posterior distribution.
The approach is evaluated using a simulation study, and is illustrated by modeling the emergence times of eight permanent teeth using data from the Signal Tandmobiel study.