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

Dimitris Rizopoulos, Laura A. Hatfield, Bradley P. Carlin, Johanna J.M. Takkenberg

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

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 recent years. More recently, a new and attractive application of this type of model 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 article 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. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1385-1397
Number of pages13
JournalJournal of the American Statistical Association
Volume109
Issue number508
DOIs
StatePublished - Oct 2 2014

Keywords

  • Prognostic modeling
  • Random effects
  • Risk prediction
  • Time-dependent covariates

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