Fuzzy set regression method to evaluate the heterogeneity of misclassifications in disease screening with interval-scaled variables: Application to osteoporosis (KCIS No. 26)

Li Sheng Chen, Ming Fang Yen, Yueh Hsia Chiu, Hsiu Hsi Chen

研究成果: 雜誌貢獻文章同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
頁(從 - 到)261-276
頁數16
期刊International Journal of Biostatistics
10
發行號2
DOIs
出版狀態已發佈 - 十一月 1 2014

ASJC Scopus subject areas

  • Medicine(all)
  • Statistics, Probability and Uncertainty
  • Statistics and Probability

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