Modeling biomarker variability in joint analysis of longitudinal and time-to-event data.

ABSTRACT: The role of visit-to-visit variability of a biomarker in predicting related disease has been recognized in medical science. Existing measures of biological variability are criticized for being entangled with random variability resulted from measurement error or being unreliable due to limited measurements per individual. In this article, we propose a new measure to quantify the biological variability of a biomarker by evaluating the fluctuation of each individual-specific trajectory behind longitudinal measurements. Given a mixed-effects model for longitudinal data with the mean function over time specified by cubic splines, our proposed variability measure can be mathematically expressed as a quadratic form of random effects. A Cox model is assumed for time-to-event data by incorporating

SUBMITTER: Wang C 

PROVIDER: S-EPMC11017116 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature