Joint linear and nonlinear mixed effects model with random change points for left-censored longitudinal data: application to HIV surveillance.

Authors

Binod Manandhar, Hongbin Zhang

Published in

Journal of biopharmaceutical statistics. Volume 36. Issue 6. Pages 1511-1521. Epub Aug 22, 2025.

Abstract

A change-point model is useful for longitudinal data to infer the time to an event that induces a change of trajectory within the study period. We present a random change-point model that fits the linear and non-linear mixed effects for pre- and post-change-points, respectively. We address the left-censored observations in the model. The stochastic approximation expectation maximization (SAEM) with the Metropolis Hastings sampler is used to fit the random change-point non-linear mixed effects model. We apply our method to the viral load (VL) longitudinal data reported to the HIV surveillance registry in New York City. We evaluate the model with a data simulation.

PMID:
42935216
Bibliographic data and abstract were imported from PubMed on 27 Oct 2026.

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