Response to Letters Regarding Article, “Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction”
TLDR
The c statistic, or area under the receiver operating characteristic (ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are relevant to discriminating diseased versus nondiseased patients, may not be optimal in assessing models that predict future risk or stratify individuals into risk categories.Abstract:
I am pleased that Pepe et al agree that the C-statistic is not a relevant measure of clinical value despite its use as such in the clinical literature They remain, however, interested in sensitivity and specificity It is true that in the high-density lipoprotein example the sensitivity, or probability of inclusion in the highest risk group among cases, improves in the model with high-density lipoprotein, whereas the specificity, or probability of inclusion in the lowest risk group among controls, declines The model with high-density lipoprotein …read more
Citations
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Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond
TL;DR: Two new measures, one based on integrated sensitivity and specificity and the other on reclassification tables, are introduced that offer incremental information over the AUC and are proposed to be considered in addition to the A UC when assessing the performance of newer biomarkers.
Journal ArticleDOI
Particulate Matter Air Pollution and Cardiovascular Disease An Update to the Scientific Statement From the American Heart Association
Robert D. Brook,Sanjay Rajagopalan,C. Arden Pope,Jeffrey R. Brook,Aruni Bhatnagar,Ana V. Diez-Roux,Fernando Holguin,Yuling Hong,Russell V. Luepker,Murray A. Mittleman,Annette Peters,David S. Siscovick,Sidney C. Smith,Laurie P. Whitsel,Joel D. Kaufman +14 more
TL;DR: It is the opinion of the writing group that the overall evidence is consistent with a causal relationship between PM2.5 exposure and cardiovascular morbidity and mortality.
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Updating and Validating the Charlson Comorbidity Index and Score for Risk Adjustment in Hospital Discharge Abstracts Using Data From 6 Countries
Hude Quan,Bing Li,Chantal Marie Couris,Kiyohide Fushimi,Patrick Graham,Phil Hider,Jean-Marie Januel,Vijaya Sundararajan +7 more
TL;DR: The updated Charlson index of 12 comorbidities showed good-to-excellent discrimination in predicting in-hospital mortality in data from 6 countries and may be more appropriate for use with more recent administrative data.
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Assessing the performance of prediction models: a framework for traditional and novel measures.
Ewout W. Steyerberg,Andrew J. Vickers,Nancy R. Cook,Thomas A. Gerds,Mithat Gonen,Nancy A Obuchowski,Michael J. Pencina,Michael W. Kattan +7 more
TL;DR: It is suggested that reporting discrimination and calibration will always be important for a prediction model and decision-analytic measures should be reported if the predictive model is to be used for clinical decisions.
Journal ArticleDOI
Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers
TL;DR: Net reclassification improvement offers a simple intuitive way of quantifying improvement offered by new markers and has been gaining popularity among researchers, however, several aspects of the NRI have not been studied in sufficient detail.
References
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Journal ArticleDOI
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
TL;DR: A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented and it is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a random chosen non-diseased subject.
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Prediction of Coronary Heart Disease Using Risk Factor Categories
Peter W.F. Wilson,Ralph B. D'Agostino,Daniel Levy,Albert M. Belanger,Halit Silbershatz,William B. Kannel +5 more
TL;DR: A simple coronary disease prediction algorithm was developed using categorical variables, which allows physicians to predict multivariate CHD risk in patients without overt CHD.
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Evaluating the yield of medical tests
TL;DR: The treadmill exercise test is shown to provide surprisingly little prognostic information beyond that obtained from basic clinical measurements.
Journal ArticleDOI
A comparison of goodness-of-fit tests for the logistic regression model.
TL;DR: An examination of the performance of the tests when the correct model has a quadratic term but a model containing only the linear term has been fit shows that the Pearson chi-square, the unweighted sum-of-squares, the Hosmer-Lemeshow decile of risk, the smoothed residual sum- of-Squares and Stukel's score test, have power exceeding 50 per cent to detect moderate departures from linearity.
Journal ArticleDOI
Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic, Prognostic, or Screening Marker
TL;DR: An illustration of the relation between odds ratios and receiver operating characteristic curves shows, for example, that a marker with an odds ratio of as high as 3 is in fact a very poor classification tool.
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