Segmental modeling of viral load changes for HIV longitudinal data with skewness and detection limits.

Although it is a common practice to analyze complex HIV longitudinal data using nonlinear mixed-effects or nonparametric mixed-effects models in literature, the following issues may standout. (i) In clinical practice, the profile of each subject's viral response may follow a 'broken-stick'-like trajectory, indicating multiple phases of decline and increase in response. Such multiple phases (change points) may be an important indicator to help quantify treatment effect and improve management of patient care. To estimate change points, nonlinear mixed-effects or nonparametric mixed-effects models become a challenge because of complicated structures of model formulations. (ii) The commonly assumed distribution for model random errors is normal, but this assumption may unrealistically obscure important features of subject variations. (iii) The response observations (viral load) may be subject to left censoring due to a limit of detection. Inferential procedures can be complicated dramatically when data with asymmetric (skewed) characteristics and left censoring are observed in conjunction with change points as unknown parameters into models. There is relatively little work concerning all these features simultaneously. This article proposes segmental mixed-effects models with skew distributions for the response process (with left censoring) under a Bayesian framework. A real data example is used to illustrate the proposed methods.