WiSER: Robust and scalable estimation and inference of within-subject variances from intensive longitudinal data

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 10(3) similar to 10(5) 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.

Automated summary

The availability of longitudinal data from electronic health records and wearable devices has opened up new research questions. In many studies, individual variability of a longitudinal outcome is as important as the mean. This article proposes a scalable method, WiSER, for estimating and inferring the effects of predictors on within-subject variance. It is robust and computationally efficient.