|
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)
|
|
Abstract:
|
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.
