方差作为健康结果的预测因子：受试者水平的性激素轨迹和变异性预测绝经期和绝经后妇女的体脂变化
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
摘要：
在现代流行病学研究中，常规收集纵向生物标志物数据和横截面结果，通常目的是为定制的早期干预决策提供信息。例如，雌二醇和卵泡刺激素等激素可以预测中年妇女健康状况的变化。大多数现有的方法侧重于从平均标记轨迹构建预测器。然而，受试者水平的生物标志物变异性也可能提供有关疾病风险和健康结局的关键信息。在本文中，我们开发了一个联合模型，估计纵向生物标志物的受试者水平的均值和方差，以预测横截面的健康结果。仿真结果表明，真正的模型参数恢复良好。相对于忽略受试者水平方差差异或进行两阶段估计（其中将估计的标记物方差视为观察值）的替代方法，所提出的方法提供了偏倚较小且有效估计。对女性健康数据的分析显示，E2的较大变异性或FSH的较大变异性与绝经过渡期脂肪量变化水平较高和瘦体重变化水平较高相关。
英文摘要：
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.
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