HIV viral dynamic models with dropouts and missing covariates

• Published in: Statistics in medicine Vol. 26; no. 17; pp. 3342 - 3357
• Main Author: Wu, Lang
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 30.07.2007; Wiley Subscription Services, Inc
• Subjects: approximate method; Bias; British Columbia; Drug resistance; HIV; HIV Infections - virology; Human immunodeficiency virus; Humans; longitudinal data; missing data; Models, Statistical; Monte-Carlo EM; nonlinear mixed-effects model; Patient Dropouts; Research Design - statistics & numerical data; Statistical analysis; Statistical methods; Viral Load
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.2816

In recent years HIV viral dynamic models have received great attention in AIDS studies. Often, subjects in these studies may drop out for various reasons such as drug intolerance or drug resistance, and covariates may also contain missing data. Statistical analyses ignoring informative dropouts and missing covariates may lead to misleading results. We consider appropriate methods for HIV viral dynamic models with informative dropouts and missing covariates and evaluate these methods via simulations. A real data set is analysed, and the results show that the initial viral decay rate, which may reflect the efficacy of the anti‐HIV treatment, may be over‐estimated if dropout patients are ignored. We also find that the current or immediate previous viral load values may be most predictive for patients' dropout. These results may be important for HIV/AIDS studies. Copyright © 2007 John Wiley & Sons, Ltd.