Yu Mingyan, Wu Zhenke, Hicken Margaret, Elliott Michael R
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Institute for Social Research, University of Michigan, Ann Arbor, Michigan, USA.
Stat Med. 2024 Dec 30;43(30):5748-5764. doi: 10.1002/sim.10281. Epub 2024 Nov 14.
Intensive longitudinal biomarker data are increasingly common in scientific studies that seek temporally granular understanding of the role of behavioral and physiological factors in relation to outcomes of interest. Intensive longitudinal biomarker data, such as those obtained from wearable devices, are often obtained at a high frequency typically resulting in several hundred to thousand observations per individual measured over minutes, hours, or days. Often in longitudinal studies, the primary focus is on relating the means of biomarker trajectories to an outcome, and the variances are treated as nuisance parameters, although they may also be informative for the outcomes. In this paper, we propose a Bayesian hierarchical model to jointly model a cross-sectional outcome and the intensive longitudinal biomarkers. To model the variability of biomarkers and deal with the high intensity of data, we develop subject-level cubic B-splines and allow the sharing of information across individuals for both the residual variability and the random effects variability. Then different levels of variability are extracted and incorporated into an outcome submodel for inferential and predictive purposes. We demonstrate the utility of the proposed model via an application involving bio-monitoring of hertz-level heart rate information from a study on social stress.
在旨在从时间维度细致了解行为和生理因素与感兴趣的结果之间关系的科学研究中，密集纵向生物标志物数据越来越普遍。密集纵向生物标志物数据，比如从可穿戴设备获得的数据，通常以高频获取，这通常导致在几分钟、几小时或几天内对每个个体测量几百到数千次观测值。在纵向研究中，主要关注点往往是将生物标志物轨迹的均值与一个结果联系起来，并且将方差视为干扰参数，尽管它们可能对结果也具有信息价值。在本文中，我们提出一种贝叶斯分层模型，用于联合建模横截面结果和密集纵向生物标志物。为了对生物标志物的变异性进行建模并处理数据的高强度，我们开发了个体水平的三次B样条，并允许在个体之间共享关于残差变异性和随机效应变异性的信息。然后提取不同水平的变异性，并将其纳入一个结果子模型，以用于推断和预测目的。我们通过一个应用展示了所提出模型的效用，该应用涉及对一项关于社会压力的研究中赫兹级心率信息的生物监测。
