Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
Methods For Evaluating Prediction Performance Of Biomarkers And Tests, Margaret S. Pepe Phd, Holly Janes Phd
Methods For Evaluating Prediction Performance Of Biomarkers And Tests, Margaret S. Pepe Phd, Holly Janes Phd
Margaret S Pepe PhD
This chapter describes and critiques methods for evaluating the performance of markers to predict risk of a current or future clinical outcome. We consider three criteria that are important for evaluating a risk model: calibration, benefit for decision making and accurate classification. We also describe and discuss a variety of summary measures in common use for quantifying predictive information such as the area under the ROC curve and R-squared. The roles and problems with recently proposed risk reclassification approaches are discussed in detail.
Testing For Improvement In Prediction Model Performance, Margaret S. Pepe Phd, Kathleen F. Kerr Phd, Gary Longton, Zheyu Wang Phd
Testing For Improvement In Prediction Model Performance, Margaret S. Pepe Phd, Kathleen F. Kerr Phd, Gary Longton, Zheyu Wang Phd
Margaret S Pepe PhD
New methodology has been proposed in recent years for evaluating the improvement in prediction performance gained by adding a new predictor, Y, to a risk model containing a set of baseline predictors, X, for a binary outcome D. We prove theoretically that null hypotheses concerning no improvement in performance are equivalent to the simple null hypothesis that the coefficient for Y is zero in the risk model, P(D=1|X,Y). Therefore, testing for improvement in prediction performance is redundant if Y has already been shown to be a risk factor. We investigate properties of tests through simulation studies, focusing on the change …