Individual Level Variance as a Predictor of Health Outcomes

• Activity Number: 343 - Contributed Poster Presentations: Section on Bayesian Statistical Science
• Type: Contributed
• Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Irena Chen* and Zhenke Wu and Michael Elliott and Sioban D Harlow and Carrie A Karvonen-Gutierrez and Michelle M Hood
• Companies: University of Michigan and University of Michigan, Ann Arbor and University of Michigan and University of Michigan and University of Michigan and University of Michigan
• Keywords: Joint models; subject-level variance; variance component priors; Study of Women's Health Across the Nation (SWAN); follicle-stimulating hormone; oestradiol

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. For example, hormones such as oestradiol (E2) and follicle-stimulating hormone (FSH) may predict changes in womens' health during the midlife. Most existing methods focus on constructing predictors from mean marker trajectories. However, subject-level biomarker variability as a predictor may provide critical information about disease risks and health outcomes. In this paper, we develop a joint model that estimates subject-level means and variances of longitudinal predictors to predict a cross-sectional health outcome. Simulations demonstrate excellent recovery of true model parameters. The proposed method provides less biased and more efficient estimates, relative to alternative approaches that either ignore subject-level differences in the variances or perform two-stage estimation where estimated marker variances are treated as observed. Analyses of women’s health data reveal that a larger variability of E2 and higher mean levels of E2 and FSH are associated with higher levels of fat mass change.