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Analysis of Clustered and Longitudinal Binary Data Subject to Response Misclassification
Author(s) -
Neuhaus John M.
Publication year - 2002
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2002.00675.x
Subject(s) - imperfect , longitudinal data , computer science , binary data , population , property (philosophy) , closure (psychology) , econometrics , exploit , statistics , binary number , mathematics , data mining , medicine , philosophy , linguistics , arithmetic , environmental health , epistemology , computer security , economics , market economy
Summary. 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.