Using predictions from a joint model for longitudinal and survival data to inform the optimal time of intervention in an abdominal aortic aneurysm screening programme

Michael J. Sweeting*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Joint models of longitudinal and survival data can be used to predict the risk of a future event occurring based on the evolution of an endogenous biomarker measured repeatedly over time. This has led naturally to the use of dynamic predictions that update each time a new longitudinal measurement is provided. In this paper, we show how such predictions can be utilised within a fuller decision modelling framework, in particular to allow planning of future interventions for patients under a ‘watchful waiting’ care pathway. Through the objective of maximising expected life-years, the predicted risks associated with not intervening (e.g. the occurrence of severe sequelae) are balanced against risks associated with the intervention (e.g. operative risks). Our example involves patients under surveillance in an abdominal aortic aneurysm screening programme where a joint longitudinal and survival model is used to associate longitudinal measurements of aortic diameter with the risk of aneurysm rupture. We illustrate how the decision to intervene, which is currently based on a diameter measurement greater than a certain threshold, could be made more personalised and dynamic through the application of a decision modelling approach.

Original languageEnglish
Pages (from-to)1247-1260
Number of pages14
JournalBiometrical Journal
Volume59
Issue number6
DOIs
Publication statusPublished - Nov 2017
Externally publishedYes

Bibliographical note

Funding Information:
Acknowledgements This work was supported by the UK Medical Research Council (G0800270), British Heart Foundation (SP/09/002) and UK National Institute for Health Research Cambridge Biomedical Research Centre. Data from the MASS and UKSAT studies were obtained as part of the RESCAN project supported by the UK NIHR Health Technology Assessment Programme (08/30/02).

Funding Information:
This work was supported by the UK Medical Research Council (G0800270), British Heart Foundation (SP/09/002) and UK National Institute for Health Research Cambridge Biomedical Research Centre. Data from the MASS and UKSAT studies were obtained as part of the RESCAN project supported by the UK NIHR Health Technology Assessment Programme (08/30/02).

Publisher Copyright:
© 2017 The Authors. Biometrical Journal Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Keywords

  • Abdominal aortic aneurysm
  • Decision making
  • Dynamic predictions
  • Joint modelling
  • Personalised medicine

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