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Activity Number: 320 - Electronic Health Records, Causal Inference and Miscellaneous
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #318281
Title: Random Change-Point Non-linear Mixed Effects Model for left-censored longitudinal data: An application to HIV surveillance
Author(s): Binod Manandhar* and Hongbin Zhang
Companies: City University of New York and City University of New York
Keywords: Censored observation; Longitudinal data; Metropolis–Hastings sampler; Mixed-effect model; Random Change-point; Stochastic approximation expectation maximization (SAEM)

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.

Authors who are presenting talks have a * after their name.

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