Although the trade-off between the two misclassifications (false-positive fraction and false-negative fraction), corresponding to type I and type II error in statistical hypothesis testing based on Neyman-Pearson lemma, to determine the optimal cutoff in the province of evaluating the accuracy of medical diagnosis and disease screening using interval-scaled biomarkers has been attempted by the receiver operating characteristic (ROC) curve, the heterogeneity of the two misclassifications in relation to the utility or individual preference for relative weights between the two errors has been barely addressed and has increasingly gained attention in disease screening when the optimal subject-specific or subgroup-specific cutoff (the heterogeneity of ROC curve) is underscored. We proposed a fuzzy set regression method to achieve such a purpose. The proposed method was illustrated with data on screening for osteoporosis with bone mineral density.
ASJC Scopus subject areas
- Statistics, Probability and Uncertainty
- Statistics and Probability