Analysis of clustered and longitudinal binary data subject to response misclassification.
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.
Neuhaus
John M
JM
Department of Epidemiology and Biostatistics, University of California, San Francisco 94143-0560, USA. john@biostat.ucsf.edu
D001699:Biometry; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D005260:Female; D015658:HIV Infections / Q000150:complications; D006801:Humans; D017733:Leukoplakia, Hairy / Q000150:complications; D016014:Linear Models; D016015:Logistic Models; D008137:Longitudinal Studies; D008297:Male; D027383:Papillomaviridae; D030361:Papillomavirus Infections / Q000175:diagnosis; D014412:Tumor Virus Infections / Q000175:diagnosis