Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study.

ABSTRACT:

Background

Multivariate longitudinal data are under-utilized for survival analysis compared to cross-sectional data (CS - data collected once across cohort). Particularly in cardiovascular risk prediction, despite available methods of longitudinal data analysis, the value of longitudinal information has not been established in terms of improved predictive accuracy and clinical applicability.

Methods

We investigated the value of longitudinal data over and above the use of cross-sectional data via 6 distinct modeling strategies from statistics, machine learning, and deep learning that incorporate repeated measures for survival analysis of the time-to-cardiovascular event in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort. We then examined and compared the

SUBMITTER: Nguyen HT 

PROVIDER: S-EPMC9878947 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature