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 estradiol and follicle-stimulating hormone 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 may also 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 biomarkers 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 variances or perform two-stage estimation where estimated marker variances are treated as observed. Analyses of women's health data reveal larger variability of E2 or larger variability of FSH were associated with higher levels of fat mass change and higher levels of lean mass change across the menopausal transition.
翻译:在现代流行病学研究中,经常收集纵向生物标志数据和跨部门结果,目的往往是为有针对性的早期干预决定提供信息;例如,激素,如丝特拉多尔和微粒刺激激素等激素,可能会预测中年期妇女健康的变化;大多数现有方法侧重于从平均标记轨迹中建立预测器;然而,主题一级的生物标志变异性也可能提供关于疾病风险和健康结果的重要信息;在本文件中,我们开发了一个联合模型,用以估计主题层次的生物标志和纵向生物标志的差异,以预测跨部门健康结果;模拟显示真实模型参数的极好恢复;拟议方法提供了较少偏差和更有效的估计,相对于忽视主题层次差异或进行两阶段估计的替代方法,如观察所观察到的标记差异。妇女健康数据分析显示,E2或FSH更大的变异性较大,与更高级别的脂肪质变化以及更高级别的初更低质量变化有关。