Simultaneous Inference for HIV Dynamic Models with Skew-t Distribution Incorporating Mismeasured Covariate and Multiple Treatment Factors

It is a common practice to analyze AIDS longitudinal data using nonlinear mixed-effects (NLME) models with normal distribution for HIV dynamics. Normality of model errors may unrealistically obscure important features of subject variations. To partially explain between- and within-subject variations, covariates are usually introduced in such models; some covariates, however, may be often measured with substantial errors. This article, motivated by an AIDS clinical study, discusses a Bayesian NLME joint modeling approach to viral dynamic models with skew-t distribution in the presence of covariate measurement error. In this model, we fully integrate viral load response, time-varying CD4 covariate with measurement error, and time-dependent drug efficacy, which is a function of multiple treatment factors, into the data analysis. Thus, the purpose of this article is to demonstrate our models and methods with application to an AIDS clinical trial study. The results suggest that modeling HIV dynamics and virologic responses with consideration of covariate measurement error and time-varying clinical factors may be important for HIV/AIDS studies in providing quantitative guidance to better understand the virologic responses to antiretroviral treatment and to help evaluation of clinical trial design in existing therapies.