Inferring the Timing of Antiretroviral Therapy by Zero-Inflated Random Change Point Models Using Longitudinal Data Subject to Left-Censoring

We propose a new random change point model that utilizes routinely recorded individual-level HIV viral load data to estimate the timing of antiretroviral therapy (ART) initiation in people living with HIV. The change point distribution is assumed to follow a zero-inflated exponential distribution fo...

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Vydáno v:Algorithms Ročník 18; číslo 6; s. 346
Hlavní autoři: Zhang, Hongbin, Robertson, McKaylee, Braunstein, Sarah L., Hanna, David B., Felsen, Uriel R., Waldron, Levi, Nash, Denis
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.06.2025
Témata:
Algorithms
Analysis
Antiretroviral drugs
Antiviral agents
censored data
Drug therapy
Gibbs sampler
Highly active antiretroviral therapy
HIV
HIV patients
Human immunodeficiency virus
nonlinear mixed-effects model
Normal distribution
Probability distribution functions
random change point model
Random variables
Samplers
Stochastic EM
Surveillance
zero-inflated exponential distribution
New York
United States
ISSN:1999-4893, 1999-4893
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Shrnutí:We propose a new random change point model that utilizes routinely recorded individual-level HIV viral load data to estimate the timing of antiretroviral therapy (ART) initiation in people living with HIV. The change point distribution is assumed to follow a zero-inflated exponential distribution for the longitudinal data, which is also subject to left-censoring, and the underlying data-generating mechanism is a nonlinear mixed-effects model. We extend the Stochastic EM (StEM) algorithm by combining a Gibbs sampler with a Metropolis–Hastings sampling. We apply the method to real HIV data to infer the timing of ART initiation since diagnosis. Additionally, we conduct simulation studies to assess the performance of our proposed method.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a18060346