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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/370

Title: Regression modelling of weighted Kappa by using generalized estimating equations
Authors: Gonin, René
Lipsitz, Stuart R.
Fitzmaurice, Garrett M.
Molenberghs, Geert
Keywords: Categorical data
Clustered data
Issue Date: 2000
Abstract: In many clinical studies more than one observer may be rating a characteristic measured on an ordinal scale. For example, a study may involve a group of physicians rating a feature seen on a pathology specimen or a computer tomography scan. In clinical studies of this kind, the weighted κ coefficient is a popular measure of agreement for ordinally scaled ratings. Our research stems from a study in which the severity of inflammatory skin disease was rated. The investigators wished to determine and evaluate the strength of agreement between a variable number of observers taking into account patient-specific (age and gender) as well as rater-specific (whether board certified in dermatology) characteristics. This suggested modelling κ as a function of these covariates. We propose the use of generalized estimating equations to estimate the weighted κ coefficient. This approach also accommodates unbalanced data which arise when some subjects are not judged by the same set of observers. Currently an estimate of overall κ for a simple unbalanced data set without covariates involving more than two observers is unavailable. In the inflammatory skin disease study none of the covariates were significantly associated with κ, thus enabling the calculation of an overall weighted κ for this unbalanced data set. In the second motivating example (multiple sclerosis), geographic location was significantly associated with κ. In addition we also compared the results of our method with current methods of testing for heterogeneity of weighted κ coefficients across strata (geographic location) that are available for balanced data sets.
URI: http://hdl.handle.net/1942/370
DOI: 10.1111/1467-9876.00175
ISI #: 000085351200001
ISSN: 0035-9254
Category: A1
Type: Journal Contribution
Validation: ecoom, 2001
Appears in Collections: Research publications

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