Cumulative viral load as a predictor of CD4+ T-cell response to antiretroviral therapy using Bayesian statistical models.

ABSTRACT: INTRODUCTION:There are Challenges in statistically modelling immune responses to longitudinal HIV viral load exposure as a function of covariates. We define Bayesian Markov Chain Monte Carlo mixed effects models to incorporate priors and examine the effect of different distributional assumptions. We prospectively fit these models to an as-yet-unpublished data from the Tshwane District Hospital HIV treatment clinic in South Africa, to determine if cumulative log viral load, an indicator of long-term viral exposure, is a valid predictor of immune response. METHODS:Models are defined, to express 'slope', i.e. mean annual increase in CD4 counts, and 'asymptote', i.e. the odds of having a CD4 count ≥500 cells/μL during antiretroviral treatment, as a function of covariates and random-effects. We

SUBMITTER: Sempa JB 

PROVIDER: S-EPMC6853324 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature