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

• Published in: BMC medical research methodology Vol. 23; no. 1; pp. 23 - 19
• Main Authors: Nguyen, Hieu T., Vasconcellos, Henrique D., Keck, Kimberley, Reis, Jared P., Lewis, Cora E., Sidney, Steven, Lloyd-Jones, Donald M., Schreiner, Pamela J., Guallar, Eliseo, Wu, Colin O., Lima, João A.C., Ambale-Venkatesh, Bharath
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 25.01.2023; BioMed Central; BMC
• Subjects: Blood pressure; Cardiovascular disease; Cardiovascular diseases; Cardiovascular Diseases - diagnosis; Cross-Sectional Studies; Data collection; Datasets; Explainable AI; Health aspects; Heart attacks; Humans; Longitudinal data; Missing data; Patient outcomes; Personalized medicine; Precision medicine; Repeated measures; Risk factors; Risk prediction; Survival Analysis; Time series; Variables; Within-subjects design; Young Adult; Young adults; United States; Survival analysis; Time-varying covariates; SHAP; Explainable AI; Repeated measures; CARDIA; Risk prediction; TIME; Longitudinal data; Personalized medicine

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 be...