Abstract #300512


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JSM 2002 Abstract #300512
Activity Number: 152
Type: Contributed
Date/Time: Monday, August 12, 2002 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section*
Abstract - #300512
Title: A Joint Model for Nonlinear Mixed-Effects Models with Censoring and Covariates Measured with Error
Author(s): Lang Wu*+
Affiliation(s): University of British Columbia
Address: 333-6356 Agricultural Road, Vancouver, British Columbia, V6T 1Z2, Canada
Keywords: EM algorithm ; linearization ; HIV ; longitudinal data
Abstract:

In recent years AIDS researchers have shown great interest in the study of HIV viral dynamics. Nonlinear mixed-effect models (NLME) have been proposed for modeling the intra- and inter-patients' variations. The inter-patients variation often receives great attention and may be partially explained by time-varying covariates such as CD4 cell counts. Statistical analyses in these studies are complicated by the following problems: i) the viral load measurements may subject to left-censoring due to a detection limit; ii) covariates are often measured with substantial errors; and iii) covariates frequently contain missing data. In this article, we address these three problems simultaneously by jointly modeling the covariate and the response processes. We adapt a Monte-Carlo EM algorithm and a linearization procedure to estimate the model parameters. Our approach is preferable to naive methods and the two-step method in the sense that it produces less biased estimates with more reliable standard errors. We analyze a real AIDS dataset and show that the fitted model may provide good prediction for non-detectable viral loads.


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