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320 – 302 - Electronic Health Records, Causal Inference, and Miscellaneous
Random Change-Point Nonlinear Mixed Effects Model for Left-Censored Longitudinal Data: An Application to HIV Surveillance
Binod Manandhar
City University of New York
A change-point model is essential in longitudinal data to infer an individual specific time to an event that induces a change of trend. However, in general, change points are not known for population-based data. We present an unknown change-point model that fits the linear and non-linear mixed effects for pre- and post-change points. We address the left-censored observations. Through stochastic approximation expectation maximization (SAEM) with the Metropolis Hasting sampler, we fit a random change-point non-linear mixed effects model. We apply our method on the longitudinal viral load (VL) data reported to the HIV surveillance registry from New York City.