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

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
Main Authors: Hongbin Zhang, McKaylee Robertson, Sarah L. Braunstein, David B. Hanna, Uriel R. Felsen, Levi Waldron, Denis Nash
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Algorithms
Subjects:
random change point model
zero-inflated exponential distribution
nonlinear mixed-effects model
censored data
Stochastic EM
Gibbs sampler
Online Access:https://www.mdpi.com/1999-4893/18/6/346
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