Palmer Glenn, Herring Amy H, Dunson David B
Department of Statistical Science, Duke University.
Ann Appl Stat. 2025 Mar;19(1):769-797. doi: 10.1214/24-aoas1988. Epub 2025 Mar 17.
Developmental epidemiology commonly focuses on assessing the association between multiple early life exposures and childhood health. Statistical analyses of data from such studies focus on inferring the contributions of individual exposures, while also characterizing time-varying and interacting effects. Such inferences are made more challenging by correlations among exposures, nonlinearity, and the curse of dimensionality. Motivated by studying the effects of prenatal bisphenol A (BPA) and phthalate exposures on glucose metabolism in adolescence using data from the ELEMENT study, we propose a low-rank longitudinal factor regression (LowFR) model for tractable inference on flexible longitudinal exposure effects. LowFR handles highly-correlated exposures using a Bayesian dynamic factor model, which is fit jointly with a health outcome via a novel factor regression approach. The model collapses on simpler and intuitive submodels when appropriate, while expanding to allow considerable flexibility in time-varying and interaction effects when supported by the data. After demonstrating LowFR's effectiveness in simulations, we use it to analyze the ELEMENT data and find that diethyl and dibutyl phthalate metabolite levels in trimesters 1 and 2 are associated with altered glucose metabolism in adolescence.
发育流行病学通常侧重于评估多种早期生活暴露因素与儿童健康之间的关联。此类研究数据的统计分析重点在于推断个体暴露因素的作用，同时刻画随时间变化的效应以及相互作用效应。暴露因素之间的相关性、非线性以及维度诅咒使得此类推断更具挑战性。受使用ELEMENT研究数据探究产前双酚A（BPA）和邻苯二甲酸盐暴露对青少年葡萄糖代谢影响的启发，我们提出了一种低秩纵向因素回归（LowFR）模型，用于对灵活的纵向暴露效应进行易于处理的推断。LowFR使用贝叶斯动态因素模型处理高度相关的暴露因素，该模型通过一种新颖的因素回归方法与健康结局联合拟合。在适当的时候，该模型会简化为更简单、直观的子模型，而在数据支持的情况下，又会扩展以允许在随时间变化的效应和相互作用效应方面具有相当大的灵活性。在模拟中证明了LowFR的有效性后，我们用它来分析ELEMENT数据，发现孕早期和孕中期的邻苯二甲酸二乙酯和邻苯二甲酸二丁酯代谢物水平与青少年葡萄糖代谢改变有关。
