Introduction: An important application of ROC analysis is the determination of the optimal cut-point for biomarkers in diagnostic studies. This comprehensive review provides a framework of cut-point election for biomarkers in diagnostic medicine. Methods: Several methods were proposed for the selection of optional cut-points. The validity and precision of the proposed methods were discussed and the clinical application of the methods was illustrated with a practical example of clinical diagnostic data of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and malondialdehyde (MDA) for prediction of inflammatory bowel disease (IBD) patients using the NCSS software. Results: Our results in the clinical data suggested that for CRP and MDA, the calculated cut-points of the Youden index, Euclidean index, Product and Union index methods were consistent in predicting IBD patients, while for ESR, only the Euclidean and Product methods yielded similar estimates. However, the diagnostic odds ratio (DOR) method provided more extreme values for the optimal cut-point for all biomarkers analyzed. Conclusion: Overall, the four methods including the Youden index, Euclidean index, Product, and IU can produce quite similar optimal cut-points for binormal pairs with the same variance. The cut-point determined with the Youden index may not agree with the other three methods in the case of skewed distributions while DOR does not produce valid informative cut-points. Therefore, more extensive Monte Carlo simulation studies are needed to investigate the conditions of test result distributions that may lead to inconsistent findings in clinical diagnostics.

Introduction: The choice of criteria in determining optimal cut-off value is a matter of concern in quantitative diagnostic tests. Several indexes such as Youden’s index, Euclidean index, product of sensitivity and specificity in receiver operator characteristic space and diagnostic odds ratio have been used in clinical practices but their advantages and limitations are not well understood by clinicians. This study aimed to compare these methods in determining optimal cut-off values for quantitative diagnostic test. Methods: The different configurations of binormal and bilogistic distributions with equal and unequal variances for nondiseased and diseased subjects were examined. The cut-off values with increment of 0.1 in Z-scale were varied. Then, the Youden’s index, Euclidean index, product of sensitivity and specificity, and diagnostic odds ratio were calculated over various cut-off values under distributional assumptions with confirmed parameters. Results: According to the obtained data from binormal model and equal variances, the optimal cut-off values derived from Youden’s index, Euclidean index, and product method were similar but the diagnostic odds ratio yielded either extremely low or extremely high optimal cut-off value. For bilogistic pair distributions with equal variances, the Youden’s, Euclidean indexes and product method resulted in an identical cut-off value but the diagnostic odds ratio was constant over various cut-points. By both binormal and bilogistic data with more variations in nondiseased population, the Youden’s index produced a higher sensitive optimal cut-off value; but with more variation for diseased distribution, the Euclidean index showed a more sensitive optimal cut-off. For bilogistic data with unequal variance, the log(diagnostic odds ratio) had a straight line relationship over cut-off values with either positive or negative slope. Conclusion: As a measure of association, diagnostic odds ratio cannot be informative in determining an optimal cut-off value. The advantage of receiver operator characteristic analysis to obtain the optimal cut-off value is to use Youden’s index, Euclidean index, or product index which is recommended. The choice between them depends on variability of test results in diseased and nondiseased subjects and the desired sensitivity.

Background: The objective of this study was to determine the gender differences in health-related quality of life (HRQoL) and to indicate to what extent this difference can be explained by differential in demographic and chronic disease conditions in Iranian elderly people. Methods: This analysis was carried out on cross-sectional data of QoL assessment among 750 elderly individuals aged 60–90 years who were dwelling in urban population of Babol, the North of Iran. The multiple linear regression model was used to estimate the association between gender and HRQoL after controlling sociodemographic characteristics and chronic disease conditions. Results: Women had significantly lower score in HRQoL in all subscales compared with men after adjusting several confounding factors (P = 0.001). The unadjusted mean difference in overall HRQoL scores was −11.2 (95% confidence interval [CI]: −13.8, −8.6) points, but it was reduced to −6.5 (95% CI: −9.0, −3.9) points between gender after adjusting by age, educational level, living status, physical activity, smoking, abdominal obesity, diabetes, and hypertension. Conclusions: Adjusting for sociodemographic and chronic disease conditions reduced the effect size of poorer HRQoL for women compared with men but did not remove the sex differences. The poorer scores of HRQoL in Iranian women needs further physical, psychological, and social supports in elderly.