ArticleStatistics in medicine2020
A flexible nonlinear mixed effects model for HIV viral load rebound after treatment interruption.
Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Fast standard error estimation for joint models of longitudinal and time-to-event data based on stochastic EM algorithms.Biostatistics (Oxford, England) · 2025Article
- Inferring the Timing of Antiretroviral Therapy by Zero-Inflated Random Change Point Models Using Longitudinal Data Subject to Left-Censoring.Algorithms · 2025Article
- High-dimensional variable selection accounting for heterogeneity in regression coefficients across multiple data sources.The Canadian journal of statistics = Revue canadienne de statistique · 2024Article
- 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
- Smoothed simulated pseudo-maximum likelihood estimation for nonlinear mixed effects models with censored responses.Statistical methods in medical research · 2023Article
- 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
- Random Change-Point Non-linear Mixed Effects Model for left-censored longitudinal data: An application to HIV surveillance.Proceedings. American Statistical Association. Annual Meeting · 2021Article
- Stochastic Version of EM Algorithm for Nonlinear Random Change-Point Models.Proceedings of the International Conference on Statistics, Theory and Applications (ICSTA ...) · 2021Article
- Comparison of empirical and dynamic models for HIV viral load rebound after treatment interruption.Statistical communications in infectious diseases · 2020Article
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Authors and funding
6 authors.
Funding
Abstract
Characterization of HIV viral rebound after the discontinuation of antiretroviral therapy is central to HIV cure research. We propose a parametric nonlinear mixed effects model for the viral rebound trajectory, which often has a rapid rise to a peak value followed by a decrease to a viral load set point. We choose a flexible functional form that captures the shapes of viral rebound trajectories and can also provide biological insights regarding the rebound process. Each parameter can incorporate a random effect to allow for variation in parameters across individuals. Key features of viral rebound trajectories such as viral set points are represented by the parameters in the model, which facilitates assessment of intervention effects and identification of important pretreatment interruption predictors for these features. We employ a stochastic expectation-maximization (StEM) algorithm to incorporate HIV-1 RNA values that are below the lower limit of assay quantification. We evaluate the performance of our model in simulation studies and apply the proposed model to longitudinal HIV-1 viral load data from five AIDS Clinical Trials Group treatment interruption studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.