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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Bibliographic Details
Published in:	Algorithms Vol. 18; no. 6; p. 346
Main Authors:	Zhang, Hongbin, Robertson, McKaylee, Braunstein, Sarah L., Hanna, David B., Felsen, Uriel R., Waldron, Levi, Nash, Denis
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.06.2025
Subjects:	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
Online Access:	Get full text
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