2020
DOI: 10.1093/jamia/ocz228
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A tutorial on calibration measurements and calibration models for clinical prediction models

Abstract: Our primary objective is to provide the clinical informatics community with an introductory tutorial on calibration measurements and calibration models for predictive models using existing R packages and custom implemented code in R on real and simulated data. Clinical predictive model performance is commonly published based on discrimination measures, but use of models for individualized predictions requires adequate model calibration. This tutorial is intended for clinical researchers who want to evaluate pr… Show more

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Cited by 254 publications
(232 citation statements)
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“…We tested discrimination with area under the receiver operating characteristics curve (AUC) and calibration with Matthews correlation coefficient. A receiver operator characteristic (ROC) curve plots the true positive rate on y -axis against the false positive rate on x -axis ( Huang et al, 2020 ). AUC is score that measures the area under the ROC curve and it ranges from 0.50 to 1.0 with higher values meaning higher discrimination.…”
Section: Methodsmentioning
confidence: 99%
“…We tested discrimination with area under the receiver operating characteristics curve (AUC) and calibration with Matthews correlation coefficient. A receiver operator characteristic (ROC) curve plots the true positive rate on y -axis against the false positive rate on x -axis ( Huang et al, 2020 ). AUC is score that measures the area under the ROC curve and it ranges from 0.50 to 1.0 with higher values meaning higher discrimination.…”
Section: Methodsmentioning
confidence: 99%
“…Brier score simultaneously account for discrimnation and calibration. [15] A smaller Brier score indicates better performance. In addition, the gradient boosting algorithm was used to estimate the relative contributions of the predictors and draw the variable importance plot.…”
Section: Evaluation Of the Performance Of The Algorithmsmentioning
confidence: 99%
“…A receiver operator characteristic (ROC) curve plots the true positive rate on y-axis against the false positive rate on x-axis. 17 AUC is score that measures the area under the ROC curve and it ranges from 0.50 to 1.0 with higher values meaning higher discrimination. Matthews correlation coefficient (MCC) is a measure that takes into account all four predictive classes -true positive, true negative, false positive and false negative.…”
Section: Manuscript To Be Reviewedmentioning
confidence: 99%
“…18 Brier score simultaneously account for discrimnation and calibration. 17 A smaller Brier score indicates better performance. We also estimated accuracy, sensitivity and specificity.…”
Section: Manuscript To Be Reviewedmentioning
confidence: 99%
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