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