ArticleStatistical communications in infectious diseases2020
Comparison of empirical and dynamic models for HIV viral load rebound after treatment interruption.
Article in Statistical communications in infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Fast standard error estimation for joint models of longitudinal and time-to-event data based on stochastic EM algorithms.Biostatistics (Oxford, England) · 2025Article
- Adverse Events Associated with Antivirals for COVID-19: An Analysis Based on FDA Adverse Event Reporting System (FAERS).Current drug safety · 2025Article
- Understanding early HIV-1 rebound dynamics following antiretroviral therapy interruption: The importance of effector cell expansion.PLoS pathogens · 2024Article
- Understanding early HIV-1 rebound dynamics following antiretroviral therapy interruption: The importance of effector cell expansion.bioRxiv : the preprint server for biology · 2024Article
- Article
- Nonlinear mixed-effects models for HIV viral load trajectories before and after antiretroviral therapy interruption, incorporating left censoring.Statistical communications in infectious diseases · 2022Article
- Analytical Treatment Interruption in HIV Trials: Statistical and Study Design Considerations.Current HIV/AIDS reports · 2021Review
- Unified model of short- and long-term HIV viral rebound for clinical trial planning.Journal of the Royal Society, Interface · 2021Article
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8 authors.
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Abstract
objectiveTo compare empirical and mechanistic modeling approaches for describing HIV-1 RNA viral load trajectories after antiretroviral treatment interruption and for identifying factors that predict features of viral rebound process.
methodsWe apply and compare two modeling approaches in analysis of data from 346 participants in six AIDS Clinical Trial Group studies. From each separate analysis, we identify predictors for viral set points and delay in rebound. Our empirical model postulates a parametric functional form whose parameters represent different features of the viral rebound process, such as rate of rise and viral load set point. The viral dynamics model augments standard HIV dynamics models-a class of mathematical models based on differential equations describing biological mechanisms-by including reactivation of latently infected cells and adaptive immune response. We use Monolix, which makes use of a Stochastic Approximation of the Expectation-Maximization algorithm, to fit non-linear mixed effects models incorporating observations that were below the assay limit of quantification.
resultsAmong the 346 participants, the median age at treatment interruption was 42. Ninety-three percent of participants were male and sixty-five percent, white non-Hispanic. Both models provided a reasonable fit to the data and can accommodate atypical viral load trajectories. The median set points obtained from two approaches were similar: 4.44 log10 copies/mL from the empirical model and 4.59 log10 copies/mL from the viral dynamics model. Both models revealed that higher nadir CD4 cell counts and ART initiation during acute/recent phase were associated with lower viral set points and identified receiving a non-nucleoside reverse transcriptase inhibitor (NNRTI)-based pre-ATI regimen as a predictor for a delay in rebound.
conclusionAlthough based on different sets of assumptions, both models lead to similar conclusions regarding features of viral rebound process.
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