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Extending the c‐statistic to nominal polytomous outcomes: the Polytomous Discrimination Index

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Confidence intervals for an effect size measure based on the Mann–Whitney statistic. Part 1: general issues and tail‐area‐based methods
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Confidence intervals for an effect size measure based on the Mann–Whitney statistic. Part 2: asymptotic methods and evaluation
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Integrated discrimination improvement and probability‐sensitive AUC variants
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Regression Modeling Strategies
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Clinical Prediction Models
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Volume under the ROC Surface for Multi-class Problems
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A Design Framework for Instrumenting Analytic Provenance for Problem-Solving Tasks
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The area above the ordinal dominance graph and the area below the receiver operating characteristic graph
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Predicting Retroperitoneal Histology in Postchemotherapy Testicular Germ Cell Cancer: A Model Update and Multicentre Validation with More Than 1000 Patients
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Performance Measures for Prediction Models and Markers: Evaluation of Predictions and Classifications
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An improved measure for comparing diagnostic tests
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A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
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Tree Induction for Probability-Based Ranking
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The PREMM1,2,6 Model Predicts Risk of MLH1, MSH2, and MSH6 Germline Mutations Based on Cancer History
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Validity of prognostic models: When is a model clinically useful?
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Integrating the Predictiveness of a Marker with Its Performance as a Classifier
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Assessing the Performance of Prediction Models
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Relationship of Predictive Modeling to Receiver Operating Characteristics
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Assessing the incremental value of diagnostic and prognostic markers: a review and illustration
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Using Relative Utility Curves to Evaluate Risk Prediction
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Measuring the Accuracy of Diagnostic Systems
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Prognosis and prognostic research: validating a prognostic model
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The meaning and use of the area under a receiver operating characteristic (ROC) curve.
inferredcites →https://doi.org/10.1161/circulationaha.106.672402openalex-snapshot
Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction
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General Cardiovascular Risk Profile for Use in Primary Care
inferredcites →https://doi.org/10.1161/circulationaha.109.192278openalex-snapshot
Criteria for Evaluation of Novel Markers of Cardiovascular Risk
inferredcites →https://doi.org/10.1177/0272989x0002000309openalex-snapshot
Comparing Three-class Diagnostic Tests by Three-way ROC Analysis
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Decision Curve Analysis: A Novel Method for Evaluating Prediction Models
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The Area under the ROC Curve and Its Competitors
inferredcites →https://doi.org/10.1177/0272989x9701700410openalex-snapshot
Application of Treatment Thresholds to Diagnostic-test Evaluation
inferredcites →https://doi.org/10.1177/0272989x9901900110openalex-snapshot
Three-way ROCs
inferredcites →https://doi.org/10.1186/1471-2288-10-96openalex-snapshot
Polytomous diagnosis of ovarian tumors as benign, borderline, primary invasive or metastatic: development and validation of standard and kernel-based risk prediction models
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One statistical test is sufficient for assessing new predictive markers
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Prediction of residual retroperitoneal mass histology after chemotherapy for metastatic nonseminomatous germ cell tumor: multivariate analysis of individual patient data from six study groups.
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Logistic Regression Model to Distinguish Between the Benign and Malignant Adnexal Mass Before Surgery: A Multicenter Study by the International Ovarian Tumor Analysis Group
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Predicting Outcome after Traumatic Brain Injury: Development and International Validation of Prognostic Scores Based on Admission Characteristics
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Assessing the Value of Risk Predictions by Using Risk Stratification Tables

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Additional file 1 of Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm
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Proposition of polytomous discrimination index and test statistics
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