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- W1544017829 abstract "Youden’s index as a common measure of the accuracy of diagnostic test is defined by sensitivity + specificity −1 . In estimating the sum of two misclassification errors of Youden’s index, the conventional estimator, defined by = + = (x D /n D )+( x H /n H ) where is an error estimate of false negative, is a false positive error estimate, x D is the frequency of (falsely) negatively classified persons out of n D diseased groups, and x H is the frequency of (falsely) positively classified persons out of nH healthy ones, may have a considerable problem of zero variance in sparse data. The simple way to solve this problem is to add the constants c D and c H in the form of = + =(x D +c D )/(n D +2c D )+(x H +c H )(n H +2c H ) The minimum Bayes risk approach is proposed in order to find the optimum points of c D and c H . Under each arm of prior errors ranged between 0 to 0.25, the optimal value of c D and c H equals 5/14. The simulation techniques are provided to confirm that the simple adjusted estimator, c , has the best performance with the smallest average mean square errors." @default.
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- W1544017829 date "2010-01-01" @default.
- W1544017829 modified "2023-09-24" @default.
- W1544017829 title "Adjusted Estimator of the Sum of Misclassification Errors of Youden’s Index in Sparse Data of a Diagnostic Study" @default.
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