Literature DB >> 12111889

Modelling HIV viral rebound using non-linear mixed effects models.

Anthony P Fitzgerald1, Victor G DeGruttola, Florin Vaida.   

Abstract

Individuals infected with the human immunodeficiency virus type 1 (HIV-1) who initiate antiretroviral therapy typically experience a marked decline in concentrations of HIV-1 RNA in plasma. Often, however, viral rebound occurs within the first year of treatment and this rebound may be associated with resistance to antiretroviral therapy. For this reason, it is important to study the patterns of virological response of HIV-1 RNA to treatment. In particular, there is interest in the relationship between the lowest level of plasma HIV-1 RNA attained after initiation of therapy (nadir value) and the time until rebound. To investigate this question, we implement a simple and flexible non-linear mixed effects model for the trajectory of the HIV-1 RNA until rebound. This model is also consistent with biological insights into the effects of treatment. We also show how the problem of censoring of HIV-1 RNA values at the lower limit of assay quantification can be addressed using a multiple imputation scheme. The algorithm is simple to implement and is based on accessible software. Our application makes use of data from clinical trial 315 conducted by the AIDS Clinical Trials Group (ACTG 315). We find a strong relationship between HIV-1 RNA nadir and time to rebound, with potentially important consequences for the management of HIV-infected individuals. Copyright 2002 John Wiley & Sons, Ltd.

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Year:  2002        PMID: 12111889     DOI: 10.1002/sim.1155

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  9 in total

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Authors:  Florin Vaida; Anthony P Fitzgerald; Victor Degruttola
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2.  Three distinct phases of HIV-1 RNA decay in treatment-naive patients receiving raltegravir-based antiretroviral therapy: ACTG A5248.

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3.  Maximum likelihood estimation of long-term HIV dynamic models and antiviral response.

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Journal:  Biometrics       Date:  2011-03       Impact factor: 2.571

4.  Estimation and inference on correlations between biomarkers with repeated measures and left-censoring due to minimum detection levels.

Authors:  Xianhong Xie; Xiaonan Xue; Stephen J Gange; Howard D Strickler; Mimi Y Kim
Journal:  Stat Med       Date:  2012-06-19       Impact factor: 2.373

5.  A flexible nonlinear mixed effects model for HIV viral load rebound after treatment interruption.

Authors:  Rui Wang; Ante Bing; Cathy Wang; Yuchen Hu; Ronald J Bosch; Victor DeGruttola
Journal:  Stat Med       Date:  2020-04-15       Impact factor: 2.373

6.  Parameter Estimation for Differential Equation Models Using a Framework of Measurement Error in Regression Models.

Authors:  Hua Liang; Hulin Wu
Journal:  J Am Stat Assoc       Date:  2008-12-01       Impact factor: 5.033

7.  Comparison of empirical and dynamic models for HIV viral load rebound after treatment interruption.

Authors:  Ante Bing; Yuchen Hu; Melanie Prague; Alison L Hill; Jonathan Z Li; Ronald J Bosch; Victor De Gruttola; Rui Wang
Journal:  Stat Commun Infect Dis       Date:  2020-08-21

8.  Modelling human immunodeficiency virus ribonucleic acid levels with finite mixtures for censored longitudinal data.

Authors:  Bettina Grün; Kurt Hornik
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2012-03       Impact factor: 1.864

9.  The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients.

Authors:  Niko Beerenwinkel; Hesam Montazeri; Heike Schuhmacher; Patrick Knupfer; Viktor von Wyl; Hansjakob Furrer; Manuel Battegay; Bernard Hirschel; Matthias Cavassini; Pietro Vernazza; Enos Bernasconi; Sabine Yerly; Jürg Böni; Thomas Klimkait; Cristina Cellerai; Huldrych F Günthard
Journal:  PLoS Comput Biol       Date:  2013-08-29       Impact factor: 4.475

  9 in total

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