Bayesian Local Influence of Generalized Failure Time Models with Latent Variables and Multivariate Censored Data

Abstract

We develop a Bayesian local influence procedure for generalized failure time models with latent variables and multivariate censored data. We propose to use the penalized splines (P-splines) approach to formulate the unknown functions of the proposed models. We assess the effects of minor perturbations to individual observations, the prior distributions of parameters, and the sampling distribution on statistical inference through various perturbation schemes. The first-order local influence measure is used to quantify the degree of minor perturbations to different aspects of a statistical model with the use of Bayes factor as an objective function. Simulation studies show that the empirical performance of the Bayesian local influence procedure is satisfactory. An application to a study of renal disease for type 2 diabetes patients is presented.