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