The availability of vast amounts of longitudinal data from electronic health records (EHRs) and personal wearable devices opens the door to numerous new research questions. In many studies, individual variability of a longitudinal outcome is as important as the mean. Blood pressure fluctuations, glycemic variations, and mood swings are prime examples where it is critical to identify factors that affect the within-individual variability. We propose a scalable method, within-subject variance estimator by robust regression (WiSER), for the estimation and inference of the effects of both time-varying and time-invariant predictors on within-subject variance. It is robust against the misspecification of the conditional distribution of responses or the distribution of random effects. It shows similar performance as the correctly specified likelihood methods but is 10³ ∼ 10⁵ times faster. The estimation algorithm scales linearly in the total number of observations, making it applicable to massive longitudinal data sets. The effectiveness of WiSER is evaluated in extensive simulation studies. Its broad applicability is illustrated using the accelerometry data from the Women's Health Study and a clinical trial for longitudinal diabetes care.

中文翻译：

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WiSER：从密集的纵向数据中对受试者内差异进行稳健且可扩展的估计和推断


来自电子健康记录（EHR）和个人可穿戴设备的大量纵向数据的可用性为许多新的研究问题打开了大门。在许多研究中，纵向结果的个体差异与平均值一样重要。血压波动、血糖变化和情绪波动是最好的例子，识别影响个体差异的因素至关重要。我们提出了一种可扩展的方法，即通过稳健回归进行的受试者内方差估计器（WiSER），用于估计和推断时变和时不变预测变量对受试者内方差的影响。它对于响应条件分布或随机效应分布的错误指定具有鲁棒性。它显示出与正确指定的似然方法相似的性能，但为 10 ³ 〜10 ⁵快几倍。该估计算法在观测总数中呈线性扩展，使其适用于海量纵向数据集。 WiSER 的有效性通过广泛的模拟研究进行评估。使用女性健康研究和纵向糖尿病护理临床试验的加速测量数据说明了其广泛的适用性。