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On population-based measures of agreement for binary classifications

✍ Scribed by Kerrie P. Nelson; Don Edwards

Publisher
John Wiley and Sons
Year
2008
Tongue
French
Weight
183 KB
Volume
36
Category
Article
ISSN
0319-5724

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✦ Synopsis

Abstract

The authors describe a model‐based kappa statistic for binary classifications which is interpretable in the same manner as Scott's pi and Cohen's kappa, yet does not suffer from the same flaws. They compare this statistic with the data‐driven and population‐based forms of Scott's pi in a population‐based setting where many raters and subjects are involved, and inference regarding the underlying diagnostic procedure is of interest. The authors show that Cohen's kappa and Scott's pi seriously underestimate agreement between experts classifying subjects for a rare disease; in contrast, the new statistic is robust to changes in prevalence. The performance of the three statistics is illustrated with simulations and prostate cancer data.

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