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

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  • Christopher A. German
  • Janet S. Sinsheimer
  • Jin Zhou
  • Hua Zhou

Abstract

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.

Suggested Citation

  • Christopher A. German & Janet S. Sinsheimer & Jin Zhou & Hua Zhou, 2022. "WiSER: Robust and scalable estimation and inference of within‐subject variances from intensive longitudinal data," Biometrics, The International Biometric Society, vol. 78(4), pages 1313-1327, December.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:4:p:1313-1327
    DOI: 10.1111/biom.13506
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    References listed on IDEAS

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    2. Huajun Ye & Jianxin Pan, 2006. "Modelling of covariance structures in generalised estimating equations for longitudinal data," Biometrika, Biometrika Trust, vol. 93(4), pages 927-941, December.
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