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Bayesian Hierarchical Modeling of the HIV Evolutionary Response to Therapy

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  • Shane T. Jensen
  • Jared Park
  • Alexander F. Braunstein
  • Jon Mcauliffe

Abstract

A major challenge for the treatment of human immunodeficiency virus (HIV) infection is the development of therapy-resistant strains. We present a statistical model that quantifies the evolution of HIV populations when exposed to particular therapies. A hierarchical Bayesian approach is used to estimate differences in rates of nucleotide changes between treatment- and control-group sequences. Each group's rates are allowed to vary spatially along the HIV genome. We employ a coalescent structure to address the sequence diversity within the treatment and control HIV populations. We evaluate the model in simulations and estimate HIV evolution in two different applications: a conventional drug therapy and an antisense gene therapy. In both studies, we detect evidence of evolutionary escape response in the HIV population. Supplementary materials for this article are available online.

Suggested Citation

  • Shane T. Jensen & Jared Park & Alexander F. Braunstein & Jon Mcauliffe, 2013. "Bayesian Hierarchical Modeling of the HIV Evolutionary Response to Therapy," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1230-1242, December.
    • Handle: RePEc:taf:jnlasa:v:108:y:2013:i:504:p:1230-1242
    DOI: 10.1080/01621459.2013.830449
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    Cited by:

    1. Heng Liu & Caizhu Huang & Heng Lian & Xia Cui, 2023. "Hierarchical Spatially Varying Coefficient Process Regression for Modeling Net Anthropogenic Nitrogen Inputs (NANI) from the Watershed of the Yangtze River, China," Sustainability, MDPI, vol. 15(16), pages 1-15, August.

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