两阶段三角回归用于昼夜节律建模
Two-Stage Trigonometric Regression for Modeling Circadian Rhythms
AI总结：
针对昼夜节律三角回归中忽略个体峰谷时间差异导致的衰减偏差，提出精细两阶段方法，通过分阶段估计与聚合，减轻偏差、提升功效并控制错误率。
摘要：
基因表达水平、激素分泌和体内温度均以约24小时的周期振荡，或表现出昼夜节律。许多昼夜节律生物学研究调查了这些节律在不同队列间的变化，发现了异常节律与癌症、代谢综合征和睡眠障碍等疾病之间的关联。分析昼夜节律生物学数据的一个挑战是，某一现象的振荡峰值和谷值时间在不同个体间存在差异。如果在昼夜节律生物学研究中普遍使用的三角回归中未考虑这些个体差异，则群体水平振幅参数的估计可能遭受衰减偏差。这种衰减偏差可能导致不准确的研究结论。为解决衰减偏差，我们提出了一种精细的两阶段（RTS）方法，用于对研究中每个个体获得的纵向数据进行三角回归。在第一阶段，估计个体水平模型的参数。在第二阶段，将这些个体水平估计的变换聚合起来，以产生用于推断的群体水平参数估计。模拟研究表明，与忽略个体间峰值和谷值时间差异的标准两阶段（STS）方法相比，我们的RTS方法减轻了参数估计中的偏差，获得了更大的统计功效，并保持了适当的I型错误控制。唯一的例外是当振荡幅度相对于数据中的随机变异较弱且样本量较小时，参数估计和统计功效出现例外。使用皮质醇水平数据和心率数据的示例表明，与STS方法相比，我们的RTS方法获得了更大的群体水平振幅参数估计和更小的多重假设检验的$p$值。
英文摘要：
Gene expression levels, hormone secretion, and internal body temperature each oscillate over an approximately 24-hour cycle, or display circadian rhythms. Many circadian biology studies have investigated how these rhythms vary across cohorts, uncovering associations between atypical rhythms and diseases such as cancer, metabolic syndrome, and sleep disorders. A challenge in analyzing circadian biology data is that the oscillation peak and trough times for a phenomenon differ across individuals. If these individual-level differences are not accounted for in trigonometric regression, which is prevalent in circadian biology studies, then estimates of the population-level amplitude parameters can suffer from attenuation bias. This attenuation bias could lead to inaccurate study conclusions. To address attenuation bias, we propose a refined two-stage (RTS) method for trigonometric regression given longitudinal data obtained from each individual participating in a study. In the first stage, the parameters of individual-level models are estimated. In the second stage, transformations of these individual-level estimates are aggregated to produce population-level parameter estimates for inference. Simulation studies show that our RTS method mitigates bias in parameter estimation, obtains greater statistical power, and maintains appropriate type I error control when compared to the standard two-stage (STS) method, which ignores individual-level differences in peak and trough times. The only exception for parameter estimation and statistical power occurs when the oscillation amplitudes are weak relative to random variability in the data and the sample size is small. Illustrations with cortisol level data and heart rate data show that our RTS method obtains larger population-level amplitude parameter estimates and smaller $p$-values for multiple hypothesis tests when compared to the STS method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
