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.

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.

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.

The study of land use change is essential due to its significant effects on the environment and human life. Land use change modelers have mostly focused on the binary methods (e.g. urban and non-urban) rather than multiple land use changes methods. Also, most of the models used for modeling of land use changes are global parametric models (e.g. artificial neural network) and local non-parametric models (e.g. Multivariate Adaptive Regression Spline (MARS)) is rarely used to simulate multiple LUCs. Local models split the data into subsets and fit distinct models on each of the subsets. Non-parametric models do not have a fixed model structure or model structure is unknown before the modeling. On the other hand, global models perform modeling using all the available data. In addition, parametric models have a fixed structure before the modeling. In this paper, we applied one of the well-known data mining tools, called multivariate adaptive regression spline, as one of the local non-parametric models with geospatial information system and satellite images to simulate urban and agriculture land use changes for northern part of Iran including cities of Sari and Ghaem Shahr over a period of 22 years during 1992 and 2014. Landsat images are the core source for information extraction and modeling of land use change in this research. Landsat images of 1992 (TM) and 2014 (ETM+) were used for modeling the urban and agricultural land uses changes. The spatial predictors considered for urban and agriculture modeling in this area were distance to urban areas, distance to agriculture areas, distance to roads, distance to water, aspect, and slope in 1992. After the modeling, a sensitivity analysis was performed on the effective parameters of the land use changes. The results of the sensitivity analysis verified that the most important factors were distances from agricultural and urban areas as well as elevation. To assess the model performance, the receiver operating characteristics (ROC) and total operating characteristics (TOC) were used. Considering multiple thresholds, ROC reveals how strong each threshold of the generated index is in diagnosing either presence or absence of a characteristic which results in a two by two contingency table without informing the size of each entries. While preserving the important information revealing by ROC, TOC gives size information of each entry. The area under the receiver operating characteristics curve for urban and agricultural land uses were 65% and 61.01%, respectively. Also, we have labeled thresholds for 0.67 and 0.40 in total operating characteristic curves for agriculture and urban gain to show four entries in the two-by-two contingency tables, respectively. These thresholds represent the probability of land use change for pixels in the suitability maps. According to the results, the percent of observations that are reference change and have been diagnosed as change by the model are equals to 36.8% and 67.06% for these thresholds, respectively.

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.

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: Planning for obesity prevention is an important global health priority. Our aim in this study was to find the optimal cut-off points of waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR), as three anthropometric indices, for prediction of overweight and obesity. We also aimed to compare the predictive ability of these indices to introduce the best choice. Methods: In this cross-sectional study, a total of 500 subjects were investigated. Anthropometric indicators were measured using a standard protocol. We considered body mass index (BMI) as the simple and most commonly used index for measuring general obesity as the comparison indicator in the present study to assess the diagnostic value for other reported obesity indices. We also performed receiver operating characteristic (ROC) curve analysis to define the optimal cut-off points of the anthropometric indicators and the best indices for overweight and obesity. Results: The proposed optimal cut-offs for WC, WHtR, and WHR were 84 cm, 0. 48 and 0. 78 for women and 98 cm, 0. 56 and 0. 87 for men, respectively. The area under the ROC curve of WHtR (women: AUC=0. 97, 95% CI: 0. 96-0. 99 vs. men: AUC=0. 97, 95%CI: 0. 96-0. 99) and WC (women: AUC=0. 97, 95% CI, 0. 95-0. 99 vs. men: AUC=0. 98, 95% CI: 0. 97-0. 99) were greater than WHR (women: AUC=0. 79, 95% CI =0. 74-0. 85 vs. men: AUC=0. 84, 95% CI=0. 79-0. 88). Conclusion: This study demonstrated that the WC and WHtR indicators are stronger indicators compared to the others. However, further studies using desirable and also local cutoffs against more accurate techniques for body fat measurement such as computerized tumor (CT) scans and dual-energy x-ray absorptiometry (DEXA) are required.

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.