Additive mean residual life model with latent variables under right censoring

Abstract

We propose a novel additive mean residual life model to examine the effects of observable and latent risk factors on the mean residual life function of interest in the presence of right censoring. We use the factor analysis to characterize the latent risk factors on the basis of multiple observed variables. We develop a borrow-strength estimation procedure that incorporates asymptotically distribution-free generalized least square method and corrected estimating equation approach. We establish the asymptotic properties of the proposed estimators. We develop a goodness-of-fit test for model checking. We conduct simulations to evaluate the finite sample performance of the proposed method. The application to the study on chronic kidney disease for type 2 diabetic patients reveals insights into the prevention of such common diabetic complication.

Bayesian analysis of semi-parametric Cox models with latent variables

“…Pan et al 13 proposed joint modeling approaches to analyze the additive hazards model with latent variables. He and colleagues 14,15 developed proportional and additive MRL models with latent variables.…”

“…Given that the above appealing features, such kind of joint analysis has been widely applied to the practical research fields in the literature. [13][14][15]18,30 3 Bayesian analysis…”