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

• Published in: Biometrics Vol. 78; no. 4; pp. 1313 - 1327
• Main Authors: German, Christopher A., Sinsheimer, Janet S., Zhou, Jin, Zhou, Hua
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.12.2022
• Subjects: Accelerometers; accelerometry; Algorithms; Blood pressure; blood pressure variability; clinical trials; Computer Simulation; diabetes; Diabetes mellitus; electronic health record (EHR); Electronic health records; Electronic medical records; emotions; Female; glycemic variation; Humans; Inference; intraindividual variability; Longitudinal Studies; method of moments; mHealth; Models, Statistical; Probability; regression analysis; Robustness (mathematics); telemedicine; Variability; Variance; Wearable technology; Womens health; blood pressure variability; mHealth; electronic health record (EHR); method of moments; intraindividual variability; glycemic variation

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, gly...