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Binod Manandhar

City University of New York



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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

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Keywords: Censored observation, Longitudinal data, Metropolis–Hastings sampler, Mixed-effect model, Random Change-point, Stochastic approximation expectation maximization (SAEM)

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.

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