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Abstract Details

Activity Number: 252
Type: Contributed
Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #308554
Title: Regression Analysis for Differentially Misclassified Correlated Binary Outcomes
Author(s): Li Tang* and Robert H. Lyles+ and Caroline C. King and David Celantano and Yungtai Lo and Jack Sobel
Companies: Emory University and Emory University and CDC and The Johns Hopkins University and Montefiore Medical Center/Albert Einstein College of Medicine and Wayne State University School of Medicine
Address: Dept. of Biostatistics and Bioinformatics, Atlanta, GA, 30322,
Keywords: bias ; differential misclassification ; random effects ; validation
Abstract:

McNemar's test is popular for assessing a difference between proportions based on paired binary outcome data. When such outcomes are subject to misclassification, previous work extended the idea of McNemar's test to propose corrected paired-data odds ratio estimation by incorporating validation data. We build upon this work to allow inclusion of covariates in a generalized linear mixed model (GLMM) for correlated binary outcomes. A likelihood-based framework is developed to efficiently incorporate internal validation data, and covariates are also permitted to impact sensitivity and specificity parameters. Simulation studies demonstrate the precision and validity of the proposed method. An illustrative example is presented based on bacterial vaginosis data from the HIV Research Epidemiology Study (HERS).


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