New Biostatistical Methods for Analyzing HIV/AIDS Data with a Lower Limit of Quantification

New Biostatistical Methods for Analyzing HIV/AIDS Data with a Lower Limit of Quantification

                                                  Getachew A. Dagne

Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, USA

                       

 Abstract

Appropriate modeling of the limit of detection is critical in the sense that detecting small amount or concentration of agents can be extremely important to assess disease status, identify evironmental exposure or quantify levels of viral load and vaccine antibody.  In AIDS studies, for example, researchers have recently shown great interest in modeling viral load (plasma HIV-1 RNA copies) data after initiation of a potent antiretroviral (ARV) treatment. Viral load is a measure of the amount of actively replicating virus and is used as a marker of disease progression among HIV-infected patients. Viral load measurements are often subject to left censoring due to a lower limit of quantification. The below detection limit (BDL) depends upon the assay used, ranging from 500 copies/ml for the first assays available in the mid-nineties to 50 copies/ml for today's ultra sensitive assay. Despite the improvement in assay sensitivity recently, left censoring of viral load data still remains a critical issue, and the methods proposed in the literature for addressing this issue use either the observed BDL or some arbitrary value, such as half of BDL. Those approaches usually lead to biased predictions that are systematically higher than predictions based on the true unknown values of BDL. Thus, the objective of this presentation is to explore the use of flexible skew-t distributions and Bayesian methods to properly account for left-censoring, skewness and heaviness in tails of an asymmetrical distribution of viral loads.