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The Robustness of Common Measures of 2 × 2 Association to Bias Due to Misclassifications
Helena Chmura Kraemer
The American Statistician
Vol. 39, No. 4, Part 1 (Nov., 1985), pp. 286-290
Stable URL: http://www.jstor.org/stable/2683705
Page Count: 5
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The strength of association between two dichotomous characteristics A and B can be measured in many ways. All of these statistics are biased when there is misclassification, and all are prevalence dependent whether or not their population values are. Measures lacking fixed endpoints for random and perfect association, such as sensitivity, specificity, risk ratios, and odds ratio, have a bias either so unpredictable or so large that the observable and true measures of association may bear little resemblance to each other. Reexpressions of these measures that fix the endpoints and other measures with fixed endpoints, such as kappa, phi, gamma, risk difference, and attributable risk, produce attenuated estimates of their true values. Disattenuating such estimators is possible using test-retest data.
The American Statistician © 1985 American Statistical Association