Nonparametric Bayesian Inference for Mean Residual Life Functions in Survival Analysis

TL;DR

This paper introduces a flexible Bayesian nonparametric approach using Dirichlet process mixtures for inference on mean residual life functions in survival analysis, accommodating covariates and group dependence.

Contribution

It develops a novel Bayesian nonparametric model for mean residual life regression incorporating covariates and group dependence via dependent Dirichlet processes.

Findings

Flexible modeling of mean residual life as a function of covariates and time.

Effective inference demonstrated through simulated and real survival data.

Extension to multiple groups with shared and group-specific features.

Abstract

The mean residual life function is a key functional for a survival distribution. It has a practically useful interpretation as the expected remaining lifetime given survival up to a particular time point, and it also characterizes the survival distribution. However, it has received limited attention in terms of inference methods under a probabilistic modeling framework. We seek to provide general inference methodology for mean residual life regression. Survival data often include a set of predictor variables for the survival response distribution, and in many cases it is natural to include the covariates as random variables into the modeling. We thus employ Dirichlet process mixture modeling for the joint stochastic mechanism of the covariates and survival responses. This approach implies a flexible model structure for the mean residual life of the conditional response distribution, allowing general shapes for mean residual life as a function of covariates given a specific time point, as well as a function of time given particular values of the covariate vector. To expand the scope of the modeling framework, we extend the mixture model to incorporate dependence across experimental groups, such as treatment and control groups. This extension is built from a dependent Dirichlet process prior for the group-specific mixing distributions, with common locations and weights that vary across groups through latent bivariate Beta distributed random variables. We develop properties of the regression models, and discuss methods for prior specification and posterior inference. The different components of the methodology are illustrated with simulated data examples, and the model is also applied to a data set comprising right censored survival times.