An Estimating Equations Approach for Modelling Kappa

Abstract

Agreement between raters for binary outcome data is typically assessed using the kappa coefficient. There has been considerable recent work extending logistic regression to provide summary estimates of interrater agreement adjusted for covariates predictive of the marginal probability of classification by each rater. We propose an estimating equations approach which can also be used to identify covariates predictive of kappa. Models may include an arbitrary and variable number of raters per subject and yet do not require any stringent parametric assumptions. Examples used to illustrate this procedure include an investigation of factors affecting agreement between primary and proxy respondents from a case-control study and a study of the effects of gender and zygosity on twin concordance for smoking history.

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