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Biostatistics Seminar Series: Inferring Within-Subject Variances From Intensive Longitudinal Data


The Division of Biostatistics at the Department of Preventive Medicine invites you to attend the following seminar.  


Time: Monday, May 22, 2:00 PM-3:00 PM CDT   

ZOOM Virtual Room Connection: Register in advance for this meeting     

Seminar Website: https://www.eventcreate.com/e/biostatisticsseminar    

Speaker Bio: https://ph.ucla.edu/about/faculty-staff-directory/hua-zhou 


Inferring Within-Subject Variances From Intensive Longitudinal Data   


Hua Zhou, Ph.D. 

Biostatistics Department, University of California Los Angeles  


The availability of vast amounts of longitudinal data from electronic health records (EHR) 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 illustrated using the accelerometry data from the Women’s Health Study and a clinical trial for longitudinal diabetes care.