Generalized estimating equations for ordinal categorical data: arbitrary patterns of missing responses and missingness in a key covariate

Biometrics
Alicia Y Toledano, C Gatsonis

Abstract

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.

References

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Citations

Jan 1, 2008·Radiological Physics and Technology·Charles E Metz
Sep 20, 2005·The New England Journal of Medicine·Etta D PisanoUNKNOWN Digital Mammographic Imaging Screening Trial (DMIST) Investigators Group
Mar 7, 2014·BMC Medical Research Methodology·Brandy M RinghamDeborah H Glueck
Nov 11, 2008·Academic Radiology·Carolyn M Rutter
Jan 15, 2009·Computational Statistics & Data Analysis·John H Page, Andrea Rotnitzky
Nov 11, 2006·Biometrical Journal. Biometrische Zeitschrift·Carol Y LinAndrzej S Kosinski
Jan 9, 2013·Medical Decision Making : an International Journal of the Society for Medical Decision Making·Craig K AbbeyJohn M Boone
Mar 8, 2018·Physics in Medicine and Biology·Nancy A Obuchowski, Jennifer A Bullen
May 26, 2018·Statistics in Medicine·Molei LiuXiao-Hua Zhou
Dec 25, 2012·Lifetime Data Analysis·Bernard RosnerMei-Ling T Lee

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