Yangxin Huang, PhD, an associate professor of biostatistics, published “Segmental modeling of viral load changes for HIV longitudinal data with skewness and detection limits, Statistics in Medicine 2013; 32(2):319-334.” In the article, he investigates the clinically-meaningful and important change-points (timing) for HIV viral load longitudinal trajectories in the presence of ‘imperfect’ features of longitudinal data. The findings can help quantify treatment effect and improve management of patient care.
Dr. Huang is a researcher in the Department of Epidemiology and Biostatistics at the University of South Florida College of Public Health.
Stat Med. 2013 Jan 30;32(2):319-34. doi: 10.1002/sim.5527. Epub 2012 Jul 26.
Segmental modeling of viral load changes for HIV longitudinal data with skewness and detection limits.
Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, FL 33612, U.S.A.
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. Copyright © 2012 John Wiley & Sons, Ltd.
Copyright © 2012 John Wiley & Sons, Ltd.
[PubMed - in process]