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Longitudinal biomarker data and cross-sectional outcomes are routinelycollected in modern epidemiology studies, often with the goal of informingtailored early intervention decisions. For example, hormones such as estradioland follicle-stimulating hormone may predict changes in womens' health duringthe midlife. Most existing methods focus on constructing predictors from meanmarker trajectories. However, subject-level biomarker variability may alsoprovide critical information about disease risks and health outcomes. In thispaper, we develop a joint model that estimates subject-level means andvariances of longitudinal biomarkers to predict a cross-sectional healthoutcome. Simulations demonstrate excellent recovery of true model parameters.The proposed method provides less biased and more efficient estimates, relativeto alternative approaches that either ignore subject-level differences invariances or perform two-stage estimation where estimated marker variances aretreated as observed. Analyses of women's health data reveal larger variabilityof E2 or larger variability of FSH were associated with higher levels of fatmass change and higher levels of lean mass change across the menopausaltransition.

Variance as a predictor of health outcomes: Subject-level trajectories  and variability of sex hormones to predict body fat changes in peri- and  post-menopausal women