Individual Level Variances as a Predictor of Health Outcomes

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Irena Chen (1), Zhenke Wu (2), Sioban Harlow (2), Carrie Karvonen-Gutierrez (2), Michael Elliott (2), Michelle Hood (2)

(1) N/A, N/A, (2) University of Michigan, N/A

Zhenke Wu  
University of Michigan

Sioban Harlow  
University of Michigan

Carrie Karvonen-Gutierrez  
University of Michigan

Michael Elliott  
University of Michigan

Michelle Hood  
University of Michigan

Irena Chen  
N/A

Irena Chen  
N/A

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

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