Document Detail

Generalized estimating equations for ordinal categorical data: arbitrary patterns of missing responses and missingness in a key covariate.
MedLine Citation:
PMID:  11318205     Owner:  NLM     Status:  MEDLINE    
We propose methods for regression analysis of repeatedly measured ordinal categorical data when there is nonmonotone missingness in these responses and when a key covariate is missing depending on observables. The methods use ordinal regression models in conjunction with generalized estimating equations (GEEs). We extend the GEE methodology to accommodate arbitrary patterns of missingness in the responses when this missingness is independent of the unobserved responses. We further extend the methodology to provide correction for possible bias when missingness in knowledge of a key covariate may depend on observables. The approach is illustrated with the analysis of data from a study in diagnostic oncology in which multiple correlated receiver operating characteristic curves are estimated and corrected for possible verification bias when the true disease status is missing depending on observables.
A Y Toledano; C Gatsonis
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Biometrics     Volume:  55     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  1999 Jun 
Date Detail:
Created Date:  2001-04-24     Completed Date:  2001-05-24     Revised Date:  2007-11-14    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  488-96     Citation Subset:  IM    
Department of Anesthesia and Critical Care, The University of Chicago Medical Center, Illinois 60637, USA.
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MeSH Terms
False Positive Reactions
Lung Neoplasms / diagnosis,  pathology
Models, Statistical
Neoplasm Staging
ROC Curve
Regression Analysis*
Grant Support
U01 CA54019/CA/NCI NIH HHS; U01 CA59404-01/CA/NCI NIH HHS

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