Bayesian semiparametric growth models for measurement error and missing data in CD4/CD8 ratio: Application to AIDS Study

Abstract

In clinical research and practice, there is often an interest in assessing the effect of time varying predictors, such as CD4/CD8 ratio, on immune recovery following antiretroviral therapy. Such predictors are measured with errors, and ignoring those measurement errors during data analysis may lead to biased results. Though parametric methods have been used for reducing biases, they usually depend on untestable assumptions. To relax those assumptions, this paper presents semiparametric mixed-effect models which deal with predictors having measurement errors and missing values. We develop a fully Bayesian approach for fitting these models and discriminating between patients who are potentially progressors or nonprogressors to severe disease condition (AIDS). The proposed methods are demonstrated using real data from an AIDS clinical study.