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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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/6/346 |
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| author | Hongbin Zhang McKaylee Robertson Sarah L. Braunstein David B. Hanna Uriel R. Felsen Levi Waldron Denis Nash |
| author_facet | Hongbin Zhang McKaylee Robertson Sarah L. Braunstein David B. Hanna Uriel R. Felsen Levi Waldron Denis Nash |
| author_sort | Hongbin Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ec6605f7eaa04b5a8806ce0e28bef215 |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-ec6605f7eaa04b5a8806ce0e28bef2152025-08-20T03:26:09ZengMDPI AGAlgorithms1999-48932025-06-0118634610.3390/a18060346Inferring the Timing of Antiretroviral Therapy by Zero-Inflated Random Change Point Models Using Longitudinal Data Subject to Left-CensoringHongbin Zhang0McKaylee Robertson1Sarah L. Braunstein2David B. Hanna3Uriel R. Felsen4Levi Waldron5Denis Nash6Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY 40506, USAInstitute for Implementation Science in Population Health, City University of New York, New York, NY 10027, USABureau of Hepatitis, HIV, and Sexually Transmitted Infections, Department of Health and Mental Hygiene, New York, NY 10013, USADepartment of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USADepartment of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY 10461, USAInstitute for Implementation Science in Population Health, City University of New York, New York, NY 10027, USAInstitute for Implementation Science in Population Health, City University of New York, New York, NY 10027, USAWe 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.https://www.mdpi.com/1999-4893/18/6/346random change point modelzero-inflated exponential distributionnonlinear mixed-effects modelcensored dataStochastic EMGibbs sampler |
| spellingShingle | Hongbin Zhang McKaylee Robertson Sarah L. Braunstein David B. Hanna Uriel R. Felsen Levi Waldron Denis Nash Inferring the Timing of Antiretroviral Therapy by Zero-Inflated Random Change Point Models Using Longitudinal Data Subject to Left-Censoring Algorithms random change point model zero-inflated exponential distribution nonlinear mixed-effects model censored data Stochastic EM Gibbs sampler |
| title | Inferring the Timing of Antiretroviral Therapy by Zero-Inflated Random Change Point Models Using Longitudinal Data Subject to Left-Censoring |
| title_full | Inferring the Timing of Antiretroviral Therapy by Zero-Inflated Random Change Point Models Using Longitudinal Data Subject to Left-Censoring |
| title_fullStr | Inferring the Timing of Antiretroviral Therapy by Zero-Inflated Random Change Point Models Using Longitudinal Data Subject to Left-Censoring |
| title_full_unstemmed | Inferring the Timing of Antiretroviral Therapy by Zero-Inflated Random Change Point Models Using Longitudinal Data Subject to Left-Censoring |
| title_short | Inferring the Timing of Antiretroviral Therapy by Zero-Inflated Random Change Point Models Using Longitudinal Data Subject to Left-Censoring |
| title_sort | inferring the timing of antiretroviral therapy by zero inflated random change point models using longitudinal data subject to left censoring |
| topic | random change point model zero-inflated exponential distribution nonlinear mixed-effects model censored data Stochastic EM Gibbs sampler |
| url | https://www.mdpi.com/1999-4893/18/6/346 |
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