This review provides the basic principle and rational for ROC analysis of rating and continuous diagnostic test results versus a gold standard. Derived indexes of accuracy, in particular area under the curve (AUC) has a meaningful interpretation for disease classification from healthy subjects. The methods of estimate of AUC and its testing in single diagnostic test and also comparative studies, the advantage of ROC curve to determine the optimal cut off values and the issues of bias and confounding have been discussed.

Dear Editor: We read with interest the article by Abu El Makarem MA et al. (1) entitled, “Platelet count/bipolar spleen diameter ratio for the prediction of esophageal varices: The special Egyptian situation.” This study evaluated noninvasive predictors of esophageal varices in cirrhotic patients by multivariate logistic regression and receiver operating characteristic (ROC) curve analysis. However, in our opinion, some aspects of the statistical analysis could be clarified. The first issue concerns the ROC analysis.The full area under the ROC curve (AUC) is a measure of the performance of the diagnostic test because it reflects the test performance at all possible cutoff levels.The AUC lies in the interval [0.5, 1], and the larger area, the better performance. A perfect test has an AUC of 1.0, whereas random chance gives an AUC of 0.5 (2, 3). In Abu El Makarem MA et al.’s paper, the AUC for age is 0.33, which is less than 0.5. Therefore, we think that something might have gone wrong when the authors performed the ROC analysis with the SPSS software. Because both the univariate analysis and multivariate logistic regression identified age as a valuable predictor of varices, older patients experienced a higher risk having varices. We presume that the AUC for age may be 0.67 (1-0.33) rather than 0.33. The second issue has to do with the odds ratio. The odds ratio refers to a ratio of the odds of the outcome occurring in one group divided by the odds of the outcome occurring in the other group with a 1-unit increase in x (4). The odds ratio for age was 1.205 in Abu El Makarem MA et al.’s (1) paper, but no statement such as “for a one year increase in age” was mentioned.

Introduction: In medical diagnostic field, evaluation of diagnostic accuracy of biomarkers or tests has always been a matter of concern. In some situations, one biomarker alone may not be sufficiently sensitive and specific for prediction of a disease. However, combining multiple biomarkers may lead to better diagnostic. The aim of this study was to assess the performance of combination of biomarkers to early detection of gestational diabetes. Materials and Methods: In the present study, the information of 523 pregnant women referring to Mahdieh and Taleghani hospitals located in Tehran city was used. Relatively, Unconjugated Estriol (uE3), Alfa-Fetoprotein (AFP), and Beta-Human Chorionic Gonadotropin (β-HCG) were measured in the early second trimester of pregnancy. The accuracy of these biomarkers and also finding optimal linear combination of them was evaluated by area under receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity. Choosing of the best cut-point was based on Youden index. Data were statistically analyzed applying R 3. 5. 1 software package. Results: In the combination of two biomarkers, the largest AUC, sensitivity, and specificity values were 0. 593, 61. 90%, and 58. 30%, respectively with optimal cut-point value 0. 11. In the combination of the three biomarkers by adjusting for age and BMI effects simultaneously, the largest AUC, sensitivity, and specificity values were 0. 751, 82. 95%, and 74. 62%, respectively with optimal cut-point value 0. 10. Conclusion: Based on the findings of this research, the linear combination of the three biomarkers by considering covariate effects improved the diagnostic performance.

Introduction: One exposes with diagnosis problems when she or he does an experiment or modeling to predict and allocate objects or persons to certain groups. For example in medicine in order to discriminate diabetes or cancers (level 2 of prevention), different criterions or indices can used. The simplest status is allocating objects to two possible categories, therefore one can measure a test variable in ordinal or continuous scale and regarding an appropriate cut-off in range of test variable and sensitivity, specificity and value of loss function, he or she can determine objects for each category. A suitable and single value index to evaluate test variable is A, area under receiver operating characteristic (ROC) curve. Since probably there are several test variables that measured on a unique sample, so there are natural correlations between As. When one wants to compare and select the best test(s) among them, ignoring of these correlations can lead to confused results.Materials and Methods: We have detailed a method to compute As and their variance-covariance matrix and introduced an adequate statistical test to compare them also using a set of simulated data have showed effectiveness of correlations on statistical results. For applied purposes we have prepared a software package using Delphi5.Results: Based on simulated data for two indices we found:A­­1 =0.660 ,SE(A1)=0.054 ,A2 =0.49 ,SE(A2)=0.06 . By ignoring correlations between A1 ,A2 we computed Z =2.1, it leads to reject equality of As in a α=0.05 level, otherwise by regarding correlation, Z =1.92 and equality will accept.Conclusion: Ignoring correlation between As can lead to incorrect results.

Multiple diagnostic tools are used by emergency physicians, every day. In addition, new tools are evaluated to obtain more accurate methods and reduce time or cost of conventional ones. In the previous parts of this educational series, we described diagnostic performance characteristics of diagnostic tests including sensitivity, specificity, positive and negative predictive values, and likelihood ratios. The receiver operating characteristics (ROC) curve is a graphical presentation of screening characteristics. ROC curve is used to determine the best cutoff point and compare two or more tests or observers by measuring the area under the curve (AUC). In this part of our educational series, we explain ROC curve and two methods to determine the best cutoff value.

Objective: To assess and compare the normal ranges of androgen hormones level, total testosterone (TT), free testosterone (FT), dehydrotestosterone (DHT), androstenedione (A4), dehydroepiandrosterone (DHEA), and dehydroepiandrosterone sulfate (DHEAS), in Iranian women based on different statistical methods. Materials and methods: This study was conducted on previous data collected in Iranian PCOS Prevalence Study, which details have been published before. A total of 1772 women of 18-45 years were recruited from urban areas of five randomly selected provinces in different geographic regions of Iran. The natural range of androgen hormones was determined and compared by two statistical methods including k-means cluster analysis, and receiver operating characteristic curve. Results: In women younger than 35 years old with any BMI, cut-off points obtained for FAI hormone were in lower percentiles; however, in older women, the results of the two methods were almost the same. Cut-off points of DHEAS in under 35 years old women of normal and obese weight and women older than 35 years old with normal weight calculated by ROC curve method was in higher percentiles than that in the cluster analysis method. In >35 years obese women, obtained cut-off points for DHEAS ROC curve was in lower percentiles in comparison to cluster analysis Conclusion: Although our study depicts the differences among the cutoff values among two statistical methods; however, lacking a gold standard test to define hyperandrogenism, we need further studies to obtain more comprehensive results.

Background & Aim: In multivariate receiver operating characteristic (MROC) curve analysis, comparing two tests is usually done by means of area under the curve (AUC’s) and sensitivities. However, the existing procedures have not addressed the issue of comparing two MROC curves when they cross each other.Methods & Materials: A modified version of AUC (mAUC) under MROC setup is proposed to address the above-mentioned problem. It is also shown that mAUC performs better than AUC. The performance of mAUC in the aspect of crossover curves is supported by a real dataset and simulation studies at different sample sizes.Results: Two real datasets, namely, Intra Uterine Growth Restricted Fetal Doppler Study (IUGRFDS) and Indian liver patient (ILP) datasets are used and apart from these simulation studies are also carried out to observe the effect of sample size. These mAUC’s are then compared with each other to show that difference exists between two curves while comparing AUC’s cannot identify the true difference existing between them. With respect to IUGRFDS dataset, MROC curves of the diagnostic procedures middle cerebral artery and cerebroplacental ratio cross each other and are found to be similar when their AUC’s and mAUC’s are compared. In ILP dataset, the extent of correct classification achieved in the case of males is shown to be better than that of females when mAUC’s at 0.5 and 0.8 are compared.Conclusion: It is observed that the mAUC’s are competent in identifying the true difference between the crossover MROC curves when the sample size is adequate, and the l values are 0.5 and 0.8 but not 0.3.

Background: Accurate temperature measurement is crucial in pediatric population. Before diagnostic tests are implemented in practice, it is suggested that their accuracy or ability to discriminate to be studied. The accuracy of a diagnostic test can be summarized in a Receiver Operating Characteristic (ROC) curve. This study was carried out to compare the accuracy of tympanic and axillary methods with rectal measurement in children less than 6 years old. Methods: A total of 220 pair of ears, axillaries, and rectal sites were used to determine the body temperature in patients aged between 3 months and 6 years, who referred to Emergency Department of Ali Asghar Hospital affiliated to Bushehr University of Medical Sciences. Rectal temperature (RT) was considered as gold standard. Fever was defined as RT >= 38 degrees C. RT, axillary, right and left tympanic temperature were measured. Measure agreement was assessed by covariate-adjusted ROC regression.Results: By comparing the area under the curves in Hanely method and the results from ROC regression analysis, we found out a significant agreement among the three measuring techniques and none of them was more accurate than the others. Conclusions: None of these techniques (axillary, right and left tympanic) was more accurate than the others and it is better to use a technique that is more convenient, painless, and safer than rectal temperature. We also propose using a modified parametric distribution-free ROC estimator which is conceptually easy and is simple to implement with the existing softwares for comparing the accuracy of medical tests.