Individualized survival predictions using state space model with longitudinal and survival data
Author(s)
Cauchi, Mark
Mills, Andrew R
Lawrie, Allan
Kiely, David G
Kadirkamanathan, Visakan
Type
Journal Article
Abstract
Monitoring disease progression often involves tracking biomarker measurements over time. Joint models (JMs) for longitudinal and survival data provide a framework to explore the relationship between time-varying biomarkers and patients' event outcomes, offering the potential for personalized survival predictions. In this article, we introduce the linear state space dynamic survival model for handling longitudinal and survival data. This model enhances the traditional linear Gaussian state space model by including survival data. It differs from the conventional JMs by offering an alternative interpretation via differential or difference equations, eliminating the need for creating a design matrix. To showcase the model's effectiveness, we conduct a simulation case study, emphasizing its performance under conditions of limited observed measurements. We also apply the proposed model to a dataset of pulmonary arterial hypertension patients, demonstrating its potential for enhanced survival predictions when compared with conventional risk scores.
Date Issued
2024-07
Date Acceptance
2024-05-21
Citation
Journal of the Royal Society Interface, 2024, 21 (216)
ISSN
1742-5662
Publisher
The Royal Society
Journal / Book Title
Journal of the Royal Society Interface
Volume
21
Issue
216
Copyright Statement
© 2024 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution
License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original
author and source are credited.
License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original
author and source are credited.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39081111
Subjects
Humans
Longitudinal Studies
Models, Statistical
Survival Analysis
expectation maximization algorithm
joint model
longitudinal data
pulmonary arterial hypertension
state space model
survival data
Publication Status
Published
Coverage Spatial
England
Date Publish Online
2024-07-31