Hierarchical Bayes Models for the Progression of HIV Infection Using Longitudinal CD4 T-Cell Numbers

Taking the absolute number of CD4 T-cells as a marker of disease progression for persons infected with the human immunodeficiency virus (HIV), we model longitudinal series of such counts for a sample of 327 subjects in the San Francisco Men's Health Study (Waves 1-8, excluding zidovudine cases)...

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Bibliographic Details
Published inJournal of the American Statistical Association Vol. 87; no. 419; pp. 615 - 626
Main Authors Lange, Nicholas, Carlin, Bradley P., Gelfand, Alan E.
Format Journal Article
LanguageEnglish
Published Alexandria, VA Taylor & Francis Group 01.09.1992
American Statistical Association
Subjects
AIDS
Applications and Case Studies
Bayesian analysis
Bayesian networks
Biological and medical sciences
Epidemiology
General aspects
Gibbs sampler
Growth curves
HIV
HIV infections
Infections
Inverse prediction
Medical sciences
Methodology
Modeling
Parametric models
Public health. Hygiene
Public health. Hygiene-occupational medicine
Random change points
Statistical discrepancies
T lymphocytes
Bayes estimation
Human
Immunopathology
Statistical analysis
Prognosis
AIDS
Hemopathy
Gibbs sampling
Epidemiology
Growth curve
Infection
Viral disease
Non linear model
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Summary:Taking the absolute number of CD4 T-cells as a marker of disease progression for persons infected with the human immunodeficiency virus (HIV), we model longitudinal series of such counts for a sample of 327 subjects in the San Francisco Men's Health Study (Waves 1-8, excluding zidovudine cases). We conduct a fully Bayesian analysis of these data. We employ individual level nonlinear models incorporating such critical features as incomplete and unbalanced data, population covariates (age at study entry and an indicator of self-reported herpes simplex virus infection), unobserved random change points, heterogeneous variances, and errors in variables. We construct prior distributions using results of previously published work from several different sources and data from HIV-negative men in this study. We also develop an approach to Bayesian model choice and individual prediction. Our analysis provides marginal posterior distributions for all population parameters in our model for this cohort. Using an inverse prediction approach, we also develop the posterior distributions of time for CD4 T-cell number to reach a specified level.
ISSN:0162-1459
1537-274X
DOI:10.1080/01621459.1992.10475258