A0520
Title: Bayesian hierarchical methods for modeling individual level variances for predicting health outcomes
Authors: Irena Chen - Max Planck Institute for Demographic Research (Germany) [presenting]
Abstract: Longitudinal biomarker data and cross-sectional outcomes are routinely collected in modern epidemiology studies, often with the goal of informing tailored early intervention decisions. Most existing methods focus on constructing predictors from mean marker trajectories. However, subject-level biomarker variability may also provide critical information about disease risks and health outcomes. Current literature does not provide statistical models to investigate such relationships with valid uncertainty quantification. A family of Bayesian hierarchical models are developed that estimates subject-level means, variances, and co-variances of longitudinal biomarkers and uses these as predictors within a joint modeling setting. These methods are designed to handle varying levels of data complexity, such as multiple marker trajectories, repeatedly measured and cross-sectional outcomes, and individual time-varying (co-)variances. Advances in personalized healthcare are supported by modeling the complex interplay between biomarker means and variances, as well as corresponding health outcomes.
