Fuzzy Set Regression Method to Evaluate the Heterogeneity of Misclassifications in Disease Screening with Interval-Scaled Variables: Application to Osteoporosis (KCIS No. 26)

Chen Li-Sheng (), Yen Ming-Fang (), Chiu Yueh-Hsia () and Chen Hsiu-Hsi ()
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Chen Li-Sheng: School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, No. 250 Wu-Hsing Street, Taipei, Taiwan
Yen Ming-Fang: School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, No. 250 Wu-Hsing Street, Taipei, Taiwan
Chiu Yueh-Hsia: Department and Graduate Institute of Health Care Management, College of Management, Chang Gung University, 259 Wen-Hwa 1st Road, Kwei-Shan Tao-Yuan, Taiwan
Chen Hsiu-Hsi: Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, Nation Taiwan University, No. 17, Hsuchow Road, Taipei, Taiwan

The International Journal of Biostatistics, 2014, vol. 10, issue 2, 261-276

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.

Keywords: fuzzy set regression method; disease screening; utility ratio (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1515/ijb-2014-0032

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