Faculty, Staff and Student Publications
Publication Date
1-1-2024
Journal
Computational Statistics & Data Analysis
Abstract
Modeling disease risk and survival using longitudinal risk factor trajectories is of interest in various clinical scenarios. The capacity to build a prognostic model using the trajectories of multiple longitudinal risk factors, in the presence of potential dependent censoring, would enable more informed, personalized decision making. A dynamic risk score modeling framework is proposed for multiple longitudinal risk factors and survival in the presence of dependent censoring, where both events depend on participants' post-baseline clinical progression and form a competing risks structure. The model requires relatively few random effects regardless of the number of longitudinal risk factors and can therefore accommodate multiple longitudinal risk factors in a parsimonious manner. The proposed method performed satisfactorily in extensive simulation studies. It is further applied to the motivating registry study on pediatric acute liver failure to model death using the trajectories of multiple clinical and biochemical markers. Once established, the model yields an easily calculable longitudinal risk score that can be used for disease monitoring among future patients.
DOI
10.1016/j.csda.2023.107837
PMID
37720873
PMCID
PMC10501111
PubMedCentral® Posted Date
1-1-2025
PubMedCentral® Full Text Version
Author MSS
Published Open-Access
yes