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

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)261-276
Number of pages16
JournalInternational Journal of Biostatistics
Volume10
Issue number2
DOIs
Publication statusPublished - Nov 1 2014

Fingerprint

Osteoporosis
Misclassification
ROC Curve
Fuzzy Sets
Screening
Regression
Receiver Operating Characteristic Curve
Interval
Evaluate
Neyman-Pearson Lemma
Bone Density
Type II error
Biomarkers
Hypothesis Testing
False Positive
Bone
Weights and Measures
Trade-offs
Subgroup
Regression method

Keywords

  • disease screening
  • fuzzy set regression method
  • utility ratio

ASJC Scopus subject areas

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

Cite this

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