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Abstract Details
Activity Number:
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461
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #302283 |
Title:
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Joint Modeling of Longitudinal and Survival Data with Missing and Left-Censored Time-Varying Covariates
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Author(s):
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Ryan C. May*+ and Joseph G. Ibrahim and Haitao Chu and Stephen R. Cole
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Companies:
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The University of North Carolina at Chapel Hill and The University of North Carolina and University of Minnesota and The University of North Carolina at Chapel Hill
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Address:
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, Chapel Hill, NC, 27599,
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Keywords:
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Joint Modeling ;
Detection Limit ;
HIV ;
MACS
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Abstract:
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We propose a joint model for longitudinal and survival data with time-varying covariates subject to detection limits and ignorable intermittent missingness. The model is motivated by data from the Multicenter Aids Cohort Study (MACS), in which HIV+ subjects have viral load and CD4 cell counts measured at repeated visits along with survival data. We model the longitudinal component of the joint model via a generalized linear mixed model (GLMM), predicting the trajectory of CD4 cell counts with viral load and other covariates. The viral load data is subject to both left-censoring due to detection limits (17\%) and ignorable intermittent missingnes (27\%). The survival component of the joint model looks at death due to AIDS, and is taken as a Cox proportional hazards model. The longitudinal and survival models are linked via the trajectory function of the GLMM, which is included in the survival component. A Bayesian analysis is conducted on the MACS data using the proposed model.
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