scholarly journals Estimation of Receiver Operating Characteristic Surface Using Mixtures of Finite Polya Trees (MFPT)

2021 ◽  
Vol 10 (2) ◽  
pp. 18
Author(s):  
Ben Kiprono Koech
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Parametric Estimation ◽  
Binary Outcomes ◽  
Polya Tree ◽  
Polya Trees ◽  
Surface Data ◽  
The Common ◽  
Non Parametric Estimation ◽  
Receiver Operating

Generalisation of Receiver operating characteristic (ROC) curve has become increasingly useful in evaluating the performance of diagnostic tests that have more than binary outcomes. While parametric approaches have been widely used over the years, the limitations associated with parametric assumptions often make it difficult to modelling the volume under surface for data that do not meet criteria under parametric distributions. As such, estimation of ROC surface using nonparametric approaches have been proposed to obtained insights on available data. One of the common approaches to non-parametric estimation is the use of Bayesian models where assumptions about priors can be made then posterior distributions obtained which can then be used to model the data. This study uses Polya tree priors where mixtures of Polya trees approach was used to model simulated three-way ROC data. The results of VUS estimation which is considered a suitable inference in evaluating performance of a diagnostic test, indicated that the mixtures of Polya trees model fitted well the ROC surface data. Further, the model performed relatively well compared to parametric and semiparametric models under similar assumptions.  

scholarly journals Comparative Assessment of Three Common Algorithms for Estimating the Variance of the Area under the Nonparametric Receiver Operating Characteristic Curve

2002 ◽  
Vol 2 (3) ◽  
pp. 280-289 ◽  
Author(s):  
Mario A. Cleves
Keyword(s):  
Receiver Operating Characteristic ◽  
Diagnostic Test ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Simulated Data ◽  
Small Samples ◽  
Binary Outcomes ◽  
Discriminatory Accuracy ◽  
Receiver Operating

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 nonparametric ROC curve are commonly used, although ambiguity exists about their behavior under diverse study conditions. Using simulated data, we found similar asymptotic performance between these algorithms when the diagnostic test produces results on a continuous scale, but found notable differences in small samples, and when the diagnostic test yields results on a discrete diagnostic scale.

scholarly journals When to consult precision-recall curves

2020 ◽  
Vol 20 (1) ◽  
pp. 131-148 ◽  
Author(s):  
Jonathan Cook ◽  
Vikram Ramadas
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Financial Analysts ◽  
Fraud Detection ◽  
Roc Curves ◽  
Binary Outcomes ◽  
Accounting Research ◽  
Receiver Operating

Receiver operating characteristic (ROC) curves are commonly used to evaluate predictions of binary outcomes. When there is a small percentage of items of interest (as would be the case with fraud detection, for example), ROC curves can provide an inflated view of performance. This can cause challenges in determining which set of predictions is better. In this article, we discuss the conditions under which precision-recall curves may be preferable to ROC curves. As an illustrative example, we compare two commonly used fraud predictors (Beneish’s [1999, Financial Analysts Journal 55: 24–36] M score and Dechow et al.’s [2011, Contemporary Accounting Research 28: 17–82] F score) using both ROC and precision-recall curves. To aid the reader with using precision-recall curves, we also introduce the command prcurve to plot them.

Übereinstimmung von klinischer Diagnose, strukturierten Interviews und Selbstbeurteilungsfragebögen bei Depression im Kindes- und Jugendalter

2012 ◽  
Vol 40 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Kathrin Dolle ◽  
Gerd Schulte-Körne ◽  
Nikolaus von Hofacker ◽  
Yonca Izat ◽  
Antje-Kathrin Allgaier
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Kinder Und Jugendliche ◽  
Prädiktive Werte ◽  
Receiver Operating

Fragestellung: Die vorliegende Studie untersucht die Übereinstimmung von strukturierten Kind- und Elterninterviews sowie dem klinischen Urteil bei der Diagnostik depressiver Episoden im Kindes- und Jugendalter. Zudem prüft sie, ob sich die Treffsicherheit und die optimalen Cut-off-Werte von Selbstbeurteilungsfragebögen in Referenz zu diesen verschiedenen Beurteilerperspektiven unterscheiden. Methodik: Mit 81 Kindern (9–12 Jahre) und 88 Jugendlichen (13–16 Jahre), die sich in kinder- und jugendpsychiatrischen Kliniken oder Praxen vorstellten, und ihren Eltern wurden strukturierte Kinder-DIPS-Interviews durchgeführt. Die Kinder füllten das Depressions-Inventar für Kinder und Jugendliche (DIKJ) aus, die Jugendlichen die Allgemeine Depressions-Skala in der Kurzform (ADS-K). Übereinstimmungen wurden mittels Kappa-Koeffizienten ermittelt. Optimale Cut-off-Werte, Sensitivität, Spezifität sowie positive und negative prädiktive Werte wurden anhand von Receiver operating characteristic (ROC) Kurven bestimmt. Ergebnisse: Die Interviews stimmten untereinander sowie mit dem klinischen Urteil niedrig bis mäßig überein. Depressive Episoden wurden häufiger nach klinischem Urteil als in den Interviews festgestellt. Cut-off-Werte und Validitätsmaße der Selbstbeurteilungsfragebögen variierten je nach Referenzstandard mit den schlechtesten Ergebnissen für das klinische Urteil. Schlussfolgerungen: Klinische Beurteiler könnten durch den Einsatz von strukturierten Interviews profitieren. Strategien für den Umgang mit diskrepanten Kind- und Elternangaben sollten empirisch geprüft und detailliert beschrieben werden.

Use of Receiver Operating Characteristic (ROC) Curves to Evaluate Computer Confidence Threshold and Clinical Performance in the Diagnosis of Appendicitis

1978 ◽  
Vol 17 (03) ◽  
pp. 157-161 ◽  
Author(s):  
F. T. De Dombal ◽  
Jane C. Horrocks
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Clinical Performance ◽  
Roc Curves ◽  
Final Diagnosis ◽  
Confidence Threshold ◽  
Computer Aided ◽  
Computer Confidence ◽  
Overall Performance ◽  
Receiver Operating

This paper uses simple receiver operating characteristic (ROC) curves (i) to study the effect of varying computer confidence of threshold levels and (ii) to evaluate clinical performance in the diagnosis of acute appendicitis. Over 1300 patients presenting to five centres with abdominal pain of short duration were studied in varying detail. Clinical and computer-aided diagnostic predictions were compared with the »final« diagnosis. From these studies it is concluded the simplistic setting of a 50/50 confidence threshold for the computer program is as »good« as any other. The proximity of a computer-aided system changed clinical behaviour patterns; a higher overall performance level was achieved and clinicians performance levels became associated with the »mildly conservative« end of the computers ROC curve. Prior forecasts of over-confidence or ultra-caution amongst clinicians using the computer-aided system have not been fulfilled.

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

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