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

Anthony P Fitzgerald1, Victor G DeGruttola, Florin Vaida

  • 1Royal College of Surgeons in Ireland, Department of Epidemiology and Public Health Medicine, Mercer Street Lower, Dublin 2, Ireland.

Statistics in Medicine
|July 12, 2002
PubMed
Summary
This summary is machine-generated.

Achieving a lower viral load nadir after starting HIV-1 therapy is linked to a longer time until viral rebound. This finding has implications for managing HIV-1 infection and treatment strategies.

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Chronic, Acute, and Reactivated HIV Infection in Humanized Immunodeficient Mouse Models
09:54

Chronic, Acute, and Reactivated HIV Infection in Humanized Immunodeficient Mouse Models

Published on: December 3, 2019

Area of Science:

  • Virology
  • Biostatistics
  • Clinical Medicine

Background:

  • Antiretroviral therapy (ART) for human immunodeficiency virus type 1 (HIV-1) typically reduces plasma HIV-1 RNA.
  • Viral rebound and potential drug resistance can occur within the first year of ART initiation.

Purpose of the Study:

  • To investigate the relationship between the nadir (lowest) plasma HIV-1 RNA level and the time to viral rebound after ART initiation.
  • To develop and apply a statistical model for analyzing HIV-1 RNA trajectories during treatment.

Main Methods:

  • Implementation of a non-linear mixed effects model to describe HIV-1 RNA decline and rebound.
  • Utilizing a multiple imputation scheme to handle censored HIV-1 RNA data below assay quantification limits.
  • Analysis of data from AIDS Clinical Trials Group (ACTG) clinical trial 315.

Main Results:

  • A significant positive correlation was identified between the HIV-1 RNA nadir value and the time until viral rebound.
  • The developed statistical model effectively captures the virological response patterns to ART.

Conclusions:

  • The nadir plasma HIV-1 RNA level is a strong predictor of time to viral rebound.
  • These findings may inform clinical management strategies for individuals with HIV-1 infection to optimize treatment outcomes.