Assessing the association between trends in a biomarker and risk of event with an application in pediatric HIV/AIDS

TL;DR

This paper introduces a joint modeling approach combining longitudinal biomarker data and survival outcomes, applied to pediatric HIV/AIDS, to better understand how biomarker trends relate to risk of events.

Contribution

A novel joint model using cubic B-splines for longitudinal data and Bayesian estimation methods, applied to pediatric HIV/AIDS to assess biomarker-risk associations.

Findings

Model effectively captures biomarker trajectories and their association with event risk.

Application to HIV data demonstrates the model's utility in clinical prognosis.

Model selection criteria improve the evaluation of joint models.

Abstract

We present a new joint longitudinal and survival model aimed at estimating the association between the risk of an event and the change in and history of a biomarker that is repeatedly measured over time. We use cubic B-splines models for the longitudinal component that lend themselves to straight-forward formulations of the slope and integral of the trajectory of the biomarker. The model is applied to data collected in a long term follow-up study of HIV infected infants in Uganda. Estimation is carried out using MCMC methods. We also explore using the deviance information criteria, the conditional predictive ordinate and ROC curves for model selection and evaluation.