Hierarchical Bayes Models for the Progression of HIV Infection Using LongitudinalCD4+ Counts

摘要

Taking the absolute number of CD4+ cells (also known as T helper cells, T4 cells,and CD4 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 331 subjects in the San Francisco Men's Health Study. We conduct a careful and fully Bayesian analysis of these data. We are able to employ individual level nonlinear models incorporating critical features such as incomplete and unbalanced data, population covariates, unobserved random change points, heterogeneous variances, and errors-invariables. Using results of previously published work from several different sources we construct rather precise prior distributions. 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+ count to reach a specified level.... AMS, Gibbs sampler, Growth curves, Heterogeneity, Inverse prediction, Marginal posterior distribution, Prior specification, Random change points, Sexual preference.