摘要：In diagnostic medicine, several measurements have been developed to evaluate the agreements among raters when the data are complete. In practice, raters may not be able to give definitive ratings to some participants because symptoms may not be clear-cut. Simply removing subjects with missing ratings may produce biased estimates and result in loss of efficiency. In this article, we propose a within-cluster resampling (WCR) procedure and a marginal approach to handle non-ignorable missing data in measurement agreement data. Simulation studies show that both WCR and marginal approach provide unbiased estimates and have coverage probabilities close to the nominal level. The proposed methods are applied to a data set from the Physician Reliability Study in diagnosing endometriosis.
关键词：Fleiss $\kappa$; Scott $\pi$; within-cluster resampling; marginal approach
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