The Statistical Evaluation of Medical Tests for Classification and Prediction

By Peihua Qiu. 2005

1,664 citations recorded by the source corpus.

Subfields: Epidemiology, Statistics and Probability, Surgery

View Paper: https://www.semanticscholar.org/paper/56ba633b3d7979f8ee2119372db9a9b05913c24d

How researchers see this paper

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

In Epidemiology

In Statistics and Probability

In Surgery

Citations in the corpus

Integrating diverse genomic data using gene sets (2011, methodology): “The ROC curves (Figure 2b) show that as a higher sensitivity is sought, the integration method still retains a substantial edge over the single-data-type methods.”
The use of receiver operating characteristic curves in biomedical informatics (2005, background): “From Pepe [33], the number of diseased subjects nD needed to test the hypothesis that AÛCa is different from AÛCb is”
Misuse of DeLong test to compare AUCs for nested models (2012, methodology): “In this situation several authors showed that LDA is the best method to estimate linear coefficients [3, 17] and presented the explicit formula for its AUC [3]”
A Probabilistic Derivation of Heidke Skill Score (2014, background): “For convenience, 1 2 FAR is defined here as the positive predictive value (PPV) and (12) as the negative predictive value (NPV) (e.g., Pepe 2003).”
The impact of Levene’s test of equality of variances on statistical theory and practice (2009, methodology): “Therefore, the authors fit an ROC curve (Gastwirth, 2001; Pepe, 2003) to the data for each radiologist using a variant of the binormal model (Dorfman and Berbaum, 2000).”
plotROC: A Tool for Plotting ROC Curves (2017, methodology): “For a more thorough reference on the methods and theory surrounding ROC curves, interested readers may look into Pepe (2003).”
Strictly Proper Scoring Rules, Prediction, and Estimation (2007, background): “This is a promising avenue for future work, particularly in concert with biomedical applications (Pepe 2003; Schumacher, Graf, and Gerds 2003).”
The inconsistency of "optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve. (2006, background): “This comparison takes place through summary measures such as the area under the curve (AUC) and the partial AUC, with higher area values indicating higher levels of diagnostic ability (1, 2, 4).”
Estimating and comparing time‐dependent areas under receiver operating characteristic curves for censored event times with competing risks (2013, background): “Without censoring, sensitivity and specificity can be estimated by empirical true positive and true negative fractions [7] and AUCs are often compared with the test of DeLong et al.”
Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine (2018, methodology): “Readers interested in ROC regression analysis should consult these references (Tosteson and Begg 1988, Pepe 1998, 2003, Pepe and Thompson 2000, Alonzo and Pepe 2002, Dodd and Pepe 2003b, Janes and Pepe 2009, Zhou et al 2011).”
The Carry Trade and Fundamentals: Nothing to Fear But FEER Itself (2009, neutral): “The biostatistics and engineering literatures provide techniques, rarely used in economics, for the statistical evaluation of models for classification (see Pepe, 2003 for a survey).”
Polyp Miss Rate Determined by Tandem Colonoscopy: A Systematic Review (2006, background): “Misclassification of the reference standard, however, can influence the perceived accuracy of the test under evaluation (5).”
A Commentary on Statistical Assessment of Violence Recidivism Risk (2015, methodology): “Note that the evaluation and validation of prediction model performance has been the subject of extensive statistical research, yielding numerous direct methods unrecognized by and superseding these authors’ confidence interval approach (Harrell et al. 1996; Harrell 2001; Pepe 2004; Steyerberg 2009a”