Wenyi Lin, Jingjing Zou, Chongzhi Di, Cheryl L Rock, Loki Natarajan
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引用次数: 0
摘要
加速度计等传感设备被广泛用于测量体力活动(PA)。这些设备提供细粒度(如 10-100 Hz 或分钟级)的输出,在提供丰富的活动模式数据的同时,也给多层次密集采样数据的计算带来了挑战,导致 PA 记录在多天和多次访问中被连续测量。另一方面,标量健康结果(如体重指数)通常只能在个人或访问水平上观测到。这就导致了预测因子(PA)和结果之间嵌套层级数量的差异,给分析带来了挑战。为解决这一问题,我们提出了多层次纵向功能主成分分析(mLFPCA)模型,以直接模拟纵向研究中的多层次功能性 PA 输入,然后实施纵向功能主成分回归(FPCR)来探讨 PA 与肥胖相关健康结果之间的关联。此外,我们还进行了一项综合模拟研究,以检验不平衡多层次数据对 mLFPCA 和 FPCR 性能的影响,并为选择最佳方法提供指导。
Multilevel Longitudinal Functional Principal Component Model.
Sensor devices, such as accelerometers, are widely used for measuring physical activity (PA). These devices provide outputs at fine granularity (e.g., 10-100 Hz or minute-level), which while providing rich data on activity patterns, also pose computational challenges with multilevel densely sampled data, resulting in PA records that are measured continuously across multiple days and visits. On the other hand, a scalar health outcome (e.g., BMI) is usually observed only at the individual or visit level. This leads to a discrepancy in numbers of nested levels between the predictors (PA) and outcomes, raising analytic challenges. To address this issue, we proposed a multilevel longitudinal functional principal component analysis (mLFPCA) model to directly model multilevel functional PA inputs in a longitudinal study, and then implemented a longitudinal functional principal component regression (FPCR) to explore the association between PA and obesity-related health outcomes. Additionally, we conducted a comprehensive simulation study to examine the impact of imbalanced multilevel data on both mLFPCA and FPCR performance and offer guidelines for selecting optimal methods.
期刊介绍:
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