Segmental Modeling of Viral Load Changes for HIV Longitudinal Data with Skewness and Detection Limits

• Activity Number: 664
• Type: Contributed
• Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Yangxin Huang*+
• Companies: University of South Florida
• Keywords: Bayesian analysis ; Change-points ; Left-censoring ; HIV/AIDS ; Segmental mixed-effects models ; Skew distributions

Abstract

Although it is a common practice to analyze complex HIV longitudinal data using NLME 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 may be an important indicator to help quantify treatment effect. To estimate change-points, NLME models become a challenge. (ii) The commonly-assumed distribution for model errors is normal, but this assumption may unrealistically obscure important features of subject variations. (iii) The response observations may be subject to left-censoring due to a limit of detection. Inferential procedures can be complicated dramatically when data with asymmetric 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 Bayesian segmental mixed-effects models with skew distributions for the response process (with left-censoring). A real data example is used to illustrate the proposed methods.