Combining Dynamic Predictions from Joint Models for Longitudinal and Time-to-Event Data using Bayesian Model Averaging

TL;DR

This paper explores how Bayesian model averaging can enhance dynamic predictions in joint models of longitudinal and time-to-event data by accounting for varying association structures and updating model weights over time.

Contribution

It introduces a method to improve prediction accuracy by combining joint models with different association structures using Bayesian model averaging with time- and subject-dependent weights.

Findings

Bayesian model averaging improves prediction accuracy.

Model weights vary over time and between subjects.

Dynamic predictions adapt as new data becomes available.

Abstract

The joint modeling of longitudinal and time-to-event data is an active area of statistics research that has received a lot of attention in the recent years. More recently, a new and attractive application of this type of models has been to obtain individualized predictions of survival probabilities and/or of future longitudinal responses. The advantageous feature of these predictions is that they are dynamically updated as extra longitudinal responses are collected for the subjects of interest, providing real time risk assessment using all recorded information. The aim of this paper is two-fold. First, to highlight the importance of modeling the association structure between the longitudinal and event time responses that can greatly influence the derived predictions, and second, to illustrate how we can improve the accuracy of the derived predictions by suitably combining joint models with different association structures. The second goal is achieved using Bayesian model averaging, which, in this setting, has the very intriguing feature that the model weights are not fixed but they are rather subject- and time-dependent, implying that at different follow-up times predictions for the same subject may be based on different models.