Division of Biostatistics, Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, California.
Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington.
Stat Med. 2024 Nov 10;43(25):4781-4795. doi: 10.1002/sim.10207. Epub 2024 Sep 3.
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.
传感器设备，如加速度计，广泛用于测量身体活动（PA）。这些设备以细粒度（例如，10-100 Hz 或分钟级）提供输出，虽然提供了关于活动模式的丰富数据，但也对多层次密集采样数据提出了计算挑战，导致 PA 记录在多天和多次就诊中连续测量。另一方面，标量健康结果（例如 BMI）通常仅在个体或就诊水平上观察到。这导致预测因子（PA）和结果之间的嵌套水平数量存在差异，从而带来分析挑战。为了解决这个问题，我们提出了一种多层次纵向函数主成分分析（mLFPCA）模型，该模型可直接在纵向研究中对多层次功能 PA 输入进行建模，然后实现纵向函数主成分回归（FPCR）来探索 PA 与肥胖相关健康结果之间的关联。此外，我们进行了全面的模拟研究，以检查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。
