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Journal articles on the topic 'ROC (receiver operating characteristic) curve'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Yang, Shengping, and Gilbert Berdine. "The receiver operating characteristic (ROC) curve." Southwest Respiratory and Critical Care Chronicles 5, no. 19 (2017): 34. http://dx.doi.org/10.12746/swrccc.v5i19.391.

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Fan, Jerome, Suneel Upadhye, and Andrew Worster. "Understanding receiver operating characteristic (ROC) curves." CJEM 8, no. 01 (2006): 19–20. http://dx.doi.org/10.1017/s1481803500013336.

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In this issue of the Journal, Auer and colleagues conclude that serum levels of neuron-specific enolase (NSE), a biochemical marker of ischemic brain injury, may have clinical utility for the prediction of survival to hospital discharge in patients experiencing the return of spontaneous circulation following at least 5 minutes of cardiopulmonary resuscitation. The authors used a receiver operating characteristic (ROC) curve to illustrate and evaluate the diagnostic (prognostic) performance of NSE. We explain ROC curve analysis in the following paragraphs.

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Pendrill, Leslie R., Jeanette Melin, Anne Stavelin, and Gunnar Nordin. "Modernising Receiver Operating Characteristic (ROC) Curves." Algorithms 16, no. 5 (2023): 253. http://dx.doi.org/10.3390/a16050253.

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The justification for making a measurement can be sought in asking what decisions are based on measurement, such as in assessing the compliance of a quality characteristic of an entity in relation to a specification limit, SL. The relative performance of testing devices and classification algorithms used in assessing compliance is often evaluated using the venerable and ever popular receiver operating characteristic (ROC). However, the ROC tool has potentially all the limitations of classic test theory (CTT) such as the non-linearity, effects of ordinality and confounding task difficulty and i

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Nahm, Francis Sahngun. "Receiver operating characteristic curve: overview and practical use for clinicians." Korean Journal of Anesthesiology 75, no. 1 (2022): 25–36. http://dx.doi.org/10.4097/kja.21209.

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Using diagnostic testing to determine the presence or absence of a disease is essential in clinical practice. In many cases, test results are obtained as continuous values and require a process of conversion and interpretation and into a dichotomous form to determine the presence of a disease. The primary method used for this process is the receiver operating characteristic (ROC) curve. The ROC curve is used to assess the overall diagnostic performance of a test and to compare the performance of two or more diagnostic tests. It is also used to select an optimal cut-off value for determining th

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Kumar, Rajeev, and Abhaya Indrayan. "Receiver operating characteristic (ROC) curve for medical researchers." Indian Pediatrics 48, no. 4 (2011): 277–87. http://dx.doi.org/10.1007/s13312-011-0055-4.

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Park, Seong Ho, Jin Mo Goo, and Chan-Hee Jo. "Receiver Operating Characteristic (ROC) Curve: Practical Review for Radiologists." Korean Journal of Radiology 5, no. 1 (2004): 11. http://dx.doi.org/10.3348/kjr.2004.5.1.11.

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Kawada, Tomoyuki. "Sample Size in Receiver-Operating Characteristic (ROC) Curve Analysis." Circulation Journal 76, no. 3 (2012): 768. http://dx.doi.org/10.1253/circj.cj-11-1408.

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Yoshinaga, Keiichiro, Kazumasa Tsukamoto, and Nagara Tamaki. "Sample Size in Receiver-Operating Characteristic (ROC) Curve Analysis." Circulation Journal 76, no. 3 (2012): 769. http://dx.doi.org/10.1253/circj.cj-11-1503.

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Cook, Jonathan A., and Ashish Rajbhandari. "Heckroccurve: ROC Curves for Selected Samples." Stata Journal: Promoting communications on statistics and Stata 18, no. 1 (2018): 174–83. http://dx.doi.org/10.1177/1536867x1801800110.

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Receiver operating characteristic (ROC) curves can be misleading when they are constructed with selected samples. In this article, we describe heckroccurve, which implements a recently developed procedure for plotting ROC curves with selected samples. The command estimates the area under the ROC curve and a graphical display of the curve. A variety of plot options are available, including the ability to add confidence bands to the plot.

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Shankar, P. M. "Parametric modeling of receiver operating characteristics curves." Model Assisted Statistics and Applications 19, no. 2 (2024): 211–21. http://dx.doi.org/10.3233/mas-231475.

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Receiver operating characteristics (ROC) curves play a pivotal role in the analyses of data collected in applications involving machine vision, machine learning and clinical diagnostics. The importance of ROC curves lies in the fact that all decision-making strategies rely on the interpretations of the curves and features extracted from them. Such analyses become simple and straightforward if it is possible to have a statistical fit for the empirical ROC curve. A methodology is developed and demonstrated to obtain a parametric fit for the ROC curves using multiple tools in statistics such as c

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Chen, Li-Pang. "Analysis of Receiver Operating Characteristic Curves for Cure Survival Data and Mismeasured Biomarkers." Mathematics 13, no. 3 (2025): 424. https://doi.org/10.3390/math13030424.

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Cure models and receiver operating characteristic (ROC) curve estimation are two important issues in survival analysis and have received attention for many years. In the development of biostatistics, these two topics have been well discussed separately. However, a rare development in the estimation of the ROC curve has been made available based on survival data with the cure fraction. On the other hand, while a large body of estimation methods have been proposed, they rely on an implicit assumption that the variables are precisely measured. In applications, measurement errors are generally ubi

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Hughes, Gareth. "On the Binormal Predictive Receiver Operating Characteristic Curve for the Joint Assessment of Positive and Negative Predictive Values." Entropy 22, no. 6 (2020): 593. http://dx.doi.org/10.3390/e22060593.

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The predictive receiver operating characteristic (PROC) curve is a diagrammatic format with application in the statistical evaluation of probabilistic disease forecasts. The PROC curve differs from the more well-known receiver operating characteristic (ROC) curve in that it provides a basis for evaluation using metrics defined conditionally on the outcome of the forecast rather than metrics defined conditionally on the actual disease status. Starting from the binormal ROC curve formulation, an overview of some previously published binormal PROC curves is presented in order to place the PROC cu

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13

Hays, Ron D. "ROC: Estimation of the Area Under a Receiver Operating Characteristic Curve." Applied Psychological Measurement 14, no. 2 (1990): 208. http://dx.doi.org/10.1177/014662169001400209.

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Morrison, Ann Michelle, Kelly Coughlin, James P. Shine, Brent A. Coull, and Andrea C. Rex. "Receiver Operating Characteristic Curve Analysis of Beach Water Quality Indicator Variables." Applied and Environmental Microbiology 69, no. 11 (2003): 6405–11. http://dx.doi.org/10.1128/aem.69.11.6405-6411.2003.

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ABSTRACT Receiver operating characteristic (ROC) curve analysis is a simple and effective means to compare the accuracies of indicator variables of bacterial beach water quality. The indicator variables examined in this study were previous day's Enterococcus density and antecedent rainfall at 24, 48, and 96 h. Daily Enterococcus densities and 15-min rainfall values were collected during a 5-year (1996 to 2000) study of four Boston Harbor beaches. The indicator variables were assessed for their ability to correctly classify water as suitable or unsuitable for swimming at a maximum threshold Ent

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Grzybowski, Mary, and John G. Younger. "Statistical Methodology: III. Receiver Operating Characteristic (ROC) Curves." Academic Emergency Medicine 4, no. 8 (1997): 818–26. http://dx.doi.org/10.1111/j.1553-2712.1997.tb03793.x.

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Tripepi, Giovanni, Kitty J. Jager, Friedo W. Dekker, and Carmine Zoccali. "Diagnostic methods 2: receiver operating characteristic (ROC) curves." Kidney International 76, no. 3 (2009): 252–56. http://dx.doi.org/10.1038/ki.2009.171.

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Han, Hyunsuk. "The Utility of Receiver Operating Characteristic Curve in Educational Assessment: Performance Prediction." Mathematics 10, no. 9 (2022): 1493. http://dx.doi.org/10.3390/math10091493.

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When examinees are classified into groups based on scores from educational assessment, two indices are widely used to gauge the psychometric quality of the classifications: accuracy and consistency. The two indices take correct classifications into consideration while overlooking incorrect ones, where unbalanced class distribution threatens the validity of results from the accuracy and consistency indices. The single values produced from the two indices also fail to address the inconsistent accuracy of the classifier across different cut score locations. The current study proposed the concept

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Gocoglu, Aylin, Neslihan Demirel, and Hamparsum Bozdogan. "A Novel Information Complexity Approach to Score Receiver Operating Characteristic (ROC) Curve Modeling." Entropy 26, no. 11 (2024): 988. http://dx.doi.org/10.3390/e26110988.

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Performance metrics are measures of success or performance that can be used to evaluate how well a model makes accurate predictions or classifications. However, there is no single measure since each performance metric emphasizes a different classification aspect. Model selection procedures based on information criteria offer a quantitative measure that balances model complexity with goodness of fit, providing a better alternative to classical approaches. In this paper, we introduce and develop a novel Information Complexity–Receiver Operating Characteristic, abbreviated as ICOMP-ROC, criterion

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Shultz, E. K. "Multivariate receiver-operating characteristic curve analysis: prostate cancer screening as an example." Clinical Chemistry 41, no. 8 (1995): 1248–55. http://dx.doi.org/10.1093/clinchem/41.8.1248.

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Abstract The evolution of test performance analysis should include the long-term costs and benefits associated with testing. Evolutionary laboratory techniques to achieve this include introduction of a new methodological technique, a multivariate extension to a current analytical technique, receiver-operating characteristic (ROC) curve analysis (MultiROC analysis). This extension to ROC methodology allows the comparison of composite test rules in a format similar to that of ROC curves. Statistical properties, guidelines for use, and a detailed example are described. MultiROC is used in the out

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Irwin, R. John, and Michael J. Hautus. "Lognormal Lorenz and normal receiver operating characteristic curves as mirror images." Royal Society Open Science 2, no. 2 (2015): 140280. http://dx.doi.org/10.1098/rsos.140280.

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The Lorenz curve for assessing economic inequality depicts the relation between two cumulative distribution functions (CDFs), one for the distribution of incomes or wealth and the other for their first-moment distribution. By contrast, the receiver operating characteristic (ROC) curve for evaluating diagnostic systems depicts the relation between the complements of two CDFs, one for the distribution noise and the other for the distribution of signal plus noise. We demonstrate that the lognormal model of the Lorenz curve, which is often adopted to model the distribution of income and wealth, is

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Takenouchi, Takashi, Osamu Komori, and Shinto Eguchi. "An Extension of the Receiver Operating Characteristic Curve and AUC-Optimal Classification." Neural Computation 24, no. 10 (2012): 2789–824. http://dx.doi.org/10.1162/neco_a_00336.

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While most proposed methods for solving classification problems focus on minimization of the classification error rate, we are interested in the receiver operating characteristic (ROC) curve, which provides more information about classification performance than the error rate does. The area under the ROC curve (AUC) is a natural measure for overall assessment of a classifier based on the ROC curve. We discuss a class of concave functions for AUC maximization in which a boosting-type algorithm including RankBoost is considered, and the Bayesian risk consistency and the lower bound of the optimu

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Vardhan, R. Vishnu, and S. Balaswamy. "Improved Methods for Estimating Areas under the Receiver Operating Characteristic Curves." International Journal of Green Computing 4, no. 2 (2013): 58–75. http://dx.doi.org/10.4018/jgc.2013070105.

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ROC Curve is the most widely used statistical technique for classifying an individual into one of the two pre-determined groups basing on test result. Area under the curve (AUC) is a measure of accuracy which exhibits the discriminating power of the test with respect to a threshold or cutoff value. In medical diagnosis, this technique has its relevance to study and compare different diagnostic tests. In this paper, a method is proposed to estimate the AUC of Binormal ROC model by taking into account the confidence interval of mean and corresponding variances.

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Moumena, Ahmed. "Anomalies Detection Based on the ROC Analysis using Classifiers in Tactical Cognitive Radio Systems: A survey." IAES International Journal of Artificial Intelligence (IJ-AI) 5, no. 3 (2016): 105. http://dx.doi.org/10.11591/ijai.v5.i3.pp105-116.

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Receiver operating characteristic (ROC) curve is an important technique for organizing classifiers and visualizing their performance in tactical systems in the presence of jamming signal. ROC curves are commonly used to evaluate the performance of classifiers for anomalies detection. This paper gives a survey of ROC analysis based on the anomaly detection using classifiers for using them in research. In recent years have been increasingly adopted in the machine learning and data mining research communities. This survey gives definitions of the anomaly detection theory and how to use one ROC cu

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Chance, Frances S. "Receiver Operating Characteristic (ROC) Analysis for Characterizing Synaptic Efficacy." Journal of Neurophysiology 97, no. 2 (2007): 1799–808. http://dx.doi.org/10.1152/jn.00885.2006.

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The role of background synaptic activity in cortical processing has recently received much attention. How do individual neurons extract information when embedded in a noisy background? When examining the impact of a synaptic input on postsynaptic firing, it is important to distinguish a change in overall firing probability from a true change in neuronal sensitivity to a particular input (synaptic efficacy) that corresponds to a change in detection performance. Here we study the impact of background synaptic input on neuronal sensitivity to individual synaptic inputs using receiver operating ch

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Song, Sang Wook. "Using the Receiver Operating Characteristic (ROC) Curve to Measure Sensitivity and Specificity." Korean Journal of Family Medicine 30, no. 11 (2009): 841. http://dx.doi.org/10.4082/kjfm.2009.30.11.841.

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Campbell, Gregory, and Makarand V. Ratnaparkhi. "An application of lomax distributions in receiver operating characteristic(roc)curve analysis." Communications in Statistics - Theory and Methods 22, no. 6 (1993): 1681–87. http://dx.doi.org/10.1080/03610929308831110.

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27

Sittig, Dean F., Jeffrey I. Clyman, Kei H. Cheung, and Perry L. Miller. "A456 RECEIVER OPERATING CHARACTERISTIC (ROC) CURVE HELPS OPTIMIZE BLOOD PRESSURE DETECTION ALGORITHMS." Anesthesiology 73, no. 3A (1990): NA. http://dx.doi.org/10.1097/00000542-199009001-00454.

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Karun, Kalesh M., and Amitha Puranik. "RECEIVER OPERATING CHARACTERISTIC CURVE ANALYSIS IN DIAGNOSTIC RESEARCH: A REVIEW." International Journal of Research in Ayurveda and Pharmacy 13, no. 3 (2022): 132–33. http://dx.doi.org/10.7897/2277-4343.130374.

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Optimal dose selection in clinical trials is problematic when efficacious and toxic concentrations are close. A receiver operating characteristic curve is a graphical technique used to identify the optimal cut-off point for a continuous variable. Implementation of ROC analysis is currently possible using various statistical software packages. However, the process is straightforward in the EZR package of R software. This present study aims to provide a tutorial using a simple example and a detailed description of the procedure in EZR software. The information provided can help the researchers p

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Hughes, Gareth, Jennifer Kopetzky, and Neil McRoberts. "Mutual Information as a Performance Measure for Binary Predictors Characterized by Both ROC Curve and PROC Curve Analysis." Entropy 22, no. 9 (2020): 938. http://dx.doi.org/10.3390/e22090938.

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The predictive receiver operating characteristic (PROC) curve differs from the more well-known receiver operating characteristic (ROC) curve in that it provides a basis for the evaluation of binary diagnostic tests using metrics defined conditionally on the outcome of the test rather than metrics defined conditionally on the actual disease status. Application of PROC curve analysis may be hindered by the complex graphical patterns that are sometimes generated. Here we present an information theoretic analysis that allows concurrent evaluation of PROC curves and ROC curves together in a simple

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Chen, Yufei, Cheng Zhang, Xianhui Liu, and Gang Wang. "Focal Liver Lesion Classification Based on Receiver Operating Characteristic Analysis." Journal of Medical Imaging and Health Informatics 9, no. 2 (2019): 284–92. http://dx.doi.org/10.1166/jmihi.2019.2609.

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Background: Computer-Aided Diagnosis (CAD) on Focal Liver Lesion (FLL) has been widely researched. It aims at classifying liver images into malignant or benign, so as to help doctors to make corresponding diagnosis. In most existing CAD systems, the automatic decision strategies on challenging cases usually lead to risky diagnosis. Objective: In this paper, we adopted a ROC optimal abstention model for FLL classification to reduce the misclassification risk. Method: The workflow of ROC based FLL classification includes the stages of feature extraction, statistic for building ROC curve and ROC

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Moise, Alain, Bernard Clement, and Marios Raissis. "A test for crossing receiver operating characteristic (roc) curves." Communications in Statistics - Theory and Methods 17, no. 6 (1988): 1985–2003. http://dx.doi.org/10.1080/03610928808829727.

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Peng, Liang, and Xiao-Hua Zhou. "Local linear smoothing of receiver operating characteristic (ROC) curves." Journal of Statistical Planning and Inference 118, no. 1-2 (2004): 129–43. http://dx.doi.org/10.1016/s0378-3758(02)00394-4.

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Kochański, Błażej. "The shape of an ROC curve in the evaluation of credit scoring models." Statistics in Transition new series 25, no. 2 (2024): 205–18. http://dx.doi.org/10.59170/stattrans-2024-022.

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The AUC, i.e. the area under the receiver operating characteristic (ROC) curve, or its scaled version, the Gini coefficient, are the standard measures of the discriminatory power of credit scoring. Using binormal ROC curve models, we show how the shape of the curves affects the economic benefits of using scoring models with the same AUC. Based on the results, we propose that the shape parameter of the fitted ROC curve is reported alongside its AUC/Gini whenever the quality of a scorecard is discussed.

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Valadares, Agnes Araujo, Paulo Schiavom Duarte, Giovanna Carvalho, et al. "Receiver operating characteristic (ROC) curve for classification of18F-NaF uptake on PET/CT." Radiologia Brasileira 49, no. 1 (2016): 12–16. http://dx.doi.org/10.1590/0100-3984.2014.0119.

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Abstract Objective: To assess the cutoff values established by ROC curves to classify18F-NaF uptake as normal or malignant. Materials and Methods: PET/CT images were acquired 1 hour after administration of 185 MBq of18F-NaF. Volumes of interest (VOIs) were drawn on three regions of the skeleton as follows: proximal right humerus diaphysis (HD), proximal right femoral diaphysis (FD) and first vertebral body (VB1), in a total of 254 patients, totalling 762 VOIs. The uptake in the VOIs was classified as normal or malignant on the basis of the radiopharmaceutical distribution pattern and of the CT

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Lavazza, Luigi, and Sandro Morasca. "Considerations on the region of interest in the ROC space." Statistical Methods in Medical Research 31, no. 3 (2021): 419–37. http://dx.doi.org/10.1177/09622802211060515.

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Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive ra

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Wandishin, Matthew S., and Steven J. Mullen. "Multiclass ROC Analysis." Weather and Forecasting 24, no. 2 (2009): 530–47. http://dx.doi.org/10.1175/2008waf2222119.1.

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Abstract Receiver operating characteristic (ROC) curves have become a common analysis tool for evaluating forecast discrimination: the ability of a forecast system to distinguish between events and nonevents. As is implicit in that statement, application of the ROC curve is limited to forecasts involving only two possible outcomes, such as rain and no rain. However, many forecast scenarios exist for which there are multiple possible outcomes, such as rain, snow, and freezing rain. An extension of the ROC curve to multiclass forecast problems is explored. The full extension involves high-dimens

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Park, Sun-Il, and Tae-Ho O. "Application of Receiver Operating Characteristic (ROC) Curve for Evaluation of Diagnostic Test Performance." Journal of Veterinary Clinics 33, no. 2 (2016): 97. http://dx.doi.org/10.17555/jvc.2016.04.33.2.97.

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Lu, Qing, Nancy Obuchowski, Sungho Won, Xiaofeng Zhu, and Robert C. Elston. "Using the Optimal Robust Receiver Operating Characteristic (ROC) Curve for Predictive Genetic Tests." Biometrics 66, no. 2 (2009): 586–93. http://dx.doi.org/10.1111/j.1541-0420.2009.01278.x.

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Kay, Elizabeth Jane, and Robin Knill-Jones. "Variation in restorative treatment decisions: application of Receiver Operating Characteristic curve (ROC) analysis." Community Dentistry and Oral Epidemiology 20, no. 3 (1992): 113–17. http://dx.doi.org/10.1111/j.1600-0528.1992.tb01542.x.

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Shidham, Vinod, Dilip Gupta, Lorenzo M. Galindo, et al. "Intraoperative scrape cytology: Comparison with frozen sections, using receiver operating characteristic (ROC) curve." Diagnostic Cytopathology 23, no. 2 (2000): 134–39. http://dx.doi.org/10.1002/1097-0339(200008)23:2<134::aid-dc14>3.0.co;2-n.

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Jang, Eun Jin, and Dal Ho Kim. "Bayesian model for the receiver operating characteristic curve using the skew normal distribution." Journal of the Korean Data And Information Science Society 32, no. 1 (2021): 15–24. http://dx.doi.org/10.7465/jkdi.2021.32.1.15.

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Kochański, Błażej. "Which Curve Fits Best: Fitting ROC Curve Models to Empirical Credit-Scoring Data." Risks 10, no. 10 (2022): 184. http://dx.doi.org/10.3390/risks10100184.

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In the practice of credit-risk management, the models for receiver operating characteristic (ROC) curves are helpful in describing the shape of an ROC curve, estimating the discriminatory power of a scorecard, and generating ROC curves without underlying data. The primary purpose of this study is to review the ROC curve models proposed in the literature, primarily in biostatistics, and to fit them to actual credit-scoring ROC data in order to determine which models could be used in credit-risk-management practice. We list several theoretical models for an ROC curve and describe them in the cre

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Ram, Baidyanath, and Vikash Kumar Singh. "Receiver Operating Characteristic Optimization Based on Convex Hull and Evolutionary Algorithm." Asian Journal of Research in Computer Science 16, no. 4 (2023): 115–24. http://dx.doi.org/10.9734/ajrcos/2023/v16i4375.

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Classification is basically a Multi-objective problem. The efficiency of classification vastly depends on performance of a classifier which can be evaluated on the basis of Receiver Operating Characteristic (ROC) graph, Area under Curve (AUC), and selection of different threshold values are generally used as a tool. In machine learning, generally, 2-D classifiers are available that deal with bi-objective problems where overlapping of class may occur i.e. sensitivity and specificity may overlap. Recently, multi-class classification in which classes are mutually exclusive is in research trends a

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Janičić, Bojan, and Zdenka Novović. "Procena uspešnosti u klasifikovanju rezultata na osnovu graničnih (cut-off) skorova: Receiver operating characteristic curve." Primenjena psihologija 4, no. 4 (2011): 335–51. http://dx.doi.org/10.19090/pp.2011.4.335-351.

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Cilj rada je da se ukaže na mogućnosti upotrebe ROC krive (engl. receiver operating characteristic curve) za utvrđivanje klasifikatornih mogućnosti testa. Objašnjeni su pojmovi senzitivnosti i specifičnosti koje leže u osnovi izrade ROC krive, a data su tumačenja i formule i za izračunavanje pozitivne i negativne prediktivne vrednosti, kao i tačnosti testa. ROC kriva je grafički prikaz senzitivnosti i specifičnosti za svaki mogući granični skor (rezultat na testu) u koordinatnom sistemu gde su na ordinati prikazane vrednosti senzitivnosti, a na apscisi vrednosti specifičnosti oduzete od 1. Obj

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Gönen, Mithat, and Glenn Heller. "Lehmann Family of ROC Curves." Medical Decision Making 30, no. 4 (2010): 509–17. http://dx.doi.org/10.1177/0272989x09360067.

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Receiver operating characteristic (ROC) curves evaluate the discriminatory power of a continuous marker to predict a binary outcome. The most popular parametric model for an ROC curve is the binormal model, which assumes that the marker, after a monotone transformation, is normally distributed conditional on the outcome. Here, the authors present an alternative to the binormal model based on the Lehmann family, also known as the proportional hazards specification. The resulting ROC curve and its functionals (such as the area under the curve and the sensitivity at a given level of specificity)

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Ransing, Ramdas S., Neha Gupta, Girish Agrawal, and Nilima Mahapatro. "Platelet and Red Blood Cell Indices in Patients with Panic Disorder: A Receiver Operating Characteristic Analysis." Journal of Neurosciences in Rural Practice 11, no. 02 (2020): 261–66. http://dx.doi.org/10.1055/s-0040-1703422.

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Abstract Objective Panic disorder (PD) is associated with changes in platelet and red blood cell (RBC) indices. However, the diagnostic or predictive value of these indices is unknown. This study assessed the diagnostic and discriminating value of platelet and RBC indices in patients with PD. Materials and Methods In this cross-sectional study including patients with PD (n = 98) and healthy controls (n = 102), we compared the following blood indices: mean platelet volume (MPV), platelet distribution width (PDW), and RBC distribution width (RDW). The receiver operating characteristic (ROC) curv

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Forbes, Thomas P., Jeffrey Lawrence, Jennifer R. Verkouteren, and R. Michael Verkouteren. "Discriminative potential of ion mobility spectrometry for the detection of fentanyl and fentanyl analogues relative to confounding environmental interferents." Analyst 144, no. 21 (2019): 6391–403. http://dx.doi.org/10.1039/c9an01771b.

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Receiver operating characteristic (ROC) curve framework was employed to investigate the trace detection of fentanyl and fifteen fentanyl-related compounds relative to environmental background interferents.

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Zweig, M. H., S. K. Broste, and R. A. Reinhart. "ROC Curve Analysis: An Example Showing the Relationships Among Serum Lipid and Apolipoprotein Concentrations in Identifying Patients with Coronary Artery Disease." Clinical Chemistry 38, no. 8 (1992): 1425–28. http://dx.doi.org/10.1093/clinchem/38.8.1425.

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Abstract Clinical accuracy, defined as the ability to discriminate between states of health, is the fundamental property of any diagnostic test or system. It is readily expressed as clinical sensitivity and specificity, and elegantly represented by the receiver operating characteristic (ROC) curve. To demonstrate the use of ROC curves, we reexamine a study of the ability of serum lipid and apolipoprotein measures to discriminate among degrees of coronary artery disease in patients undergoing coronary angiography. ROC curve analysis reveals that none of these indexes is highly accurate, but dem

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Cleves, Mario A. "Comparative Assessment of Three Common Algorithms for Estimating the Variance of the Area under the Nonparametric Receiver Operating Characteristic Curve." Stata Journal: Promoting communications on statistics and Stata 2, no. 3 (2002): 280–89. http://dx.doi.org/10.1177/1536867x0200200304.

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The area under the receiver operating characteristic (ROC) curve is often used to summarize and compare the discriminatory accuracy of a diagnostic test or modality, and to evaluate the predictive power of statistical models for binary outcomes. Parametric maximum likelihood methods for fitting of the ROC curve provide direct estimates of the area under the ROC curve and its variance. Nonparametric methods, on the other hand, provide estimates of the area under the ROC curve, but do not directly estimate its variance. Three algorithms for computing the variance for the area under the nonparame

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Xu, Fan, Linfeng Xie, Jian He, et al. "Prediction of Postoperative Acute Kidney Injury Risk Factors for Acute Type A Aortic Dissection Patients after Modified Triple-Branched Stent Graft Implantation by a Perioperative Nomogram: A Retrospective Study." Journal of Cardiac Surgery 2023 (October 31, 2023): 1–10. http://dx.doi.org/10.1155/2023/3220929.

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Objective. Predicting risk factors for acute kidney injury (AKI) after total arch replacement via modified triple-branched stent graft (MTBSG) implantation in patients with acute type A aortic dissection (AAAD) by conducting a nomogram. Methods. We collected the clinical data of 254 patients with AAAD who underwent MTBSG implantation surgery in our center. The independent risk factors of postoperative AKI were screened by univariate and multivariate logistic regression analysis and combined into a nomogram. We use receiver operating characteristic (ROC) curves, decision curve analysis (DCA), c

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