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A0293
Title: A robust joint model of longitudinal trajectories and time-to-event data at biobank scale Authors:  Hua Zhou - UCLA (United States) [presenting]
Jin Zhou - University of Arizona (United States)
Gang Li - University of California at Los Angeles (United States)
Abstract: Motivated by the analysis of massive electronic health record (EHR) and wearable device data in modern biobanks, a robust and scalable M-estimator, termed the joint model robust estimator (JMRE), is proposed for estimating the accelerated failure time (AFT) model for a right-censored event time jointly with a linear mixed model (LMM) for the longitudinal biomarker trajectory. As a semiparametric estimator, JMRE is robust to distribution misspecification in both AFT and LMM models. It is scalable to biobank data with $10^5 \sim 10^8$ individuals, intensive longitudinal measurements, and a large number of random effects. It can simultaneously model the time-varying effects on both mean and within-subject variance of the longitudinal biomarker. Furthermore, it is easily extensible to data with multiple longitudinal biomarkers.