The Statistical Evaluation of Medical Tests for Classification and Prediction

By M. Pepe. 2003

1,564 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Information Management, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1f1b750baf5b0fa427c5b38379cdcc8d70634d36

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tilmann Gneiting
2. Gary S. Collins
3. Philip Dawid
4. Peter B. Imrey
5. B. Van calster
6. Evangelia Christodoulou
7. J. Verbakel
8. Jie Ma

In Health Information Management

1. B. Van calster
2. Camilla Calì
3. Philip Dawid
4. Ilker Ünal
5. Sun-Yuan Kung
6. Chandan K. Reddy
7. Leonhard Held
8. Ewout W. Steyerberg

In Statistics and Probability

1. Gary S. Collins
2. B. Van calster
3. Tilmann Gneiting
4. I. Van Keilegom
5. F. Nahm
6. Ewout W. Steyerberg
7. Evangelia Christodoulou
8. J. Verbakel

Citations in the corpus

• Combining probability forecasts (2010, background): “Of course, there are many other important applications of probability forecasts, including but not limited to medical diagnosis (Wilson et al. 1998; Pepe 2003), educational”
• Measuring classifier performance: a coherent alternative to the area under the ROC curve (2009, methodology): “They include misclassification (or error) rate, the Kolmogorov-Smirnov (KS) statistic, likelihood ratios, the area under the ROC curve (or, equivalently, the Gini coefficient, defined below), pairs of measures such as specificity and sensitivity or precision and recall, measures of accuracy of proba”
• Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals (2013, methodology): “Following the literature on ROC curves [7,8], we denote the output values for the negative examples by X and the output values for the positive examples by Y (Z is a mixture of X and Y ).”
• ROC analysis: applications to the classification of biological sequences and 3D structures (2008, methodology): “For a complete treatise of parametric and nonparametric methods see [16–18].”
• Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach (2017, background): “The identification of the cutpoint value requires a simultaneous assessment of sensitivity and specificity [3].”
• Some mathematical properties of the ROC curve and their applications (2015, methodology): “When we suppose that test result are normally distributed we obtain the so-called binormal ROC curve (for details see [13]).”
• OptimalCutpoints: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests (2014, background): “…several optimal-cutpoint selection criteria based on Se and Sp measures (Green and Swets 1966; Zweig and Campbell 1993; Coffin and Sukhatme 1997; Pepe 2003), by imposing specifications of one kind or another in respect of such measures, assuming certain values, or defining a linear combination or…”
• Uncertainty and Inference for Verification Measures (2007, background): “Relatively little has been published on this, but see Pepe (2003).”
• What do we know and when do we know it? (2008, background): “This simple change makes the enrichment independent of extensive quantities, more robust, accessible to analytic error approximation [27] and yet suffers only a slight reduction in interpretability.”