Bayesian Joint Modelling of Longitudinal and Survival Data of HIV/AIDS Patients: A Case Study at Bale Robe General Hospital, Ethiopia

Ahmed Hasan Dessiso

doi:10.11648/j.ajtas.20170604.13

Abstract

Joint analysis of longitudinal and survival data has received increasing attention in the recent years, especially for AIDS. This study explores application of Bayesian joint modeling of HIV/AIDS data obtained from Bale Robe General Hospital, Ethiopia. The objective is to develop separate and joint statistical models in the Bayesian framework for longitudinal measurements and time to death event data of HIV/AIDS patients. A linear mixed effects model (LMEM), assuming homogenous and heterogeneous CD4 variances, is used for modeling the CD4 counts and a Weibull survival model is used for describing the time to death event. Then, both processes are linked using unobserved random effects through the use of a shared parameter model. The analysis of both the separate and the joint models reveal that the assumption of heterogeneous (patient-specific) CD4 variances brings improvement in the model fit. The Bayesian joint model is found to best fit to the data, and provided more precise estimates of parameters. The shared frailty is significant showing the association between the linear mixed effect (LME) and survival models.

Highlights

The term joint modeling refers to the statistical analysis of the longitudinal and survival data while taking account of any association between the repeated measurement and time to event outcomes
The development of joint model has greatly expanded the scope of models to accommodate many data complexities, yet relatively little attention has been paid to these approaches properties and performance
The objective of the study was to jointly analyze and build joint model for CD4 progression and time to death of HIV/AIDS patients simultaneously linked with unobserved random effects through the use of shared parameters based on data from Hospital records

Summary

Introduction

The term joint modeling refers to the statistical analysis of the longitudinal and survival data while taking account of any association between the repeated measurement and time to event outcomes. The approach that this study used to build a joint model is simultaneously modeling the longitudinal CD4 measurements and the time to death by linking them using unobserved random effects through the use of a shared parameter model. To characterize the longitudinal CD4 measurements a linear mixed effects model (LMEM) that incorporates patient specific CD4 variability is used for the longitudinal sub-model while a Weibull model is used to describe the time-to-death data of survival submodel. The two sub-models are linked through shared parameters or shared variables [1] with different forms, since these random effects characterize the subject specific longitudinal process. The two models are governed by the same underlying latent process (shared variables)

Objectives

Results

Conclusion