Analysis of clustered and longitudinal binary data subject to response misclassification.

John M Neuhaus

doi:10.1111/j.0006-341x.2002.00675.x

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Analysis of clustered and longitudinal binary data subject to response misclassification.

John M Neuhaus

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Abstract

Misclassified clustered and longitudinal data arise in studies where the response indicates a condition identified through an imperfect diagnostic procedure. Examples include longitudinal studies that use an imperfect diagnostic test to assess whether or not an individual has been infected with a specific virus. This article presents methods to implement both population-averaged and cluster-specific analyses of such data when the misclassification rates are known. The methods exploit the fact that the class of generalized linear models enjoys a closure property in the case of misclassified responses. Data from longitudinal studies of infectious disease will illustrate the findings.

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Biometry Cluster Analysis Data Interpretation, Statistical Female HIV Infections Humans Leukoplakia, Hairy Linear Models Logistic Models Longitudinal Studies Male Papillomaviridae Papillomavirus Infections Tumor Virus Infections Journal Article Research Support, U.S. Gov't, P.H.S.

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John M Neuhaus. 2002. Analysis of clustered and longitudinal binary data subject to response misclassification.. https://doi.org/10.1111/j.0006-341x.2002.00675.x

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