Individual Level Variances as a Predictor of Health Outcomes

Oregon Convention Center 

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 is associated with slower rates of waist circumference gains over the menopausal transition.

Bayesian methods

Joint models

Subject-level variability

Variance component priors

midlife aging

women's health 

ENAR