Maximum Likelihood Estimation of Agreement in the Constant Predictive Probability Model, and Its Relation to Cohen's Kappa

作者：Mikel G. Aickin · 发表于：Biometrics · 年份：1990 · DOI：10.2307/2531434 · 被引用次数：121 · 研究领域：Reliability and Agreement in Measurement、Statistical Methods in Epidemiology、Hemodynamic Monitoring and Therapy

The alpha agreement parameter is defined as the proportion of a population of items that are classified identically "for cause" by two classifiers, the remaining items being classified at random. The parameters of the corresponding constant predictive probability model are shown to be estimable by the method of maximum likelihood, and a simulation study indicates applicability of the asymptotic results to finite samples. The new estimator tends to be larger than Cohen's kappa, except in the case of uniform margins. An application is made to the validity of cancer risk items included in a cancer registry.