%0 Journal Article %1 Huang2007 %A Huang, Ying %A Pepe, Margaret Sullivan %A Feng, Ziding %D 2007 %J Biometrics %K Algorithms BiologicalMarkers BiologicalMarkers:analysis Biometry Biometry:methods Computer-Assisted Computer-Assisted:methods ComputerSimulation DataInterpretation Diagnosis Models ReproducibilityofResults SensitivityandSpecificity Statistical %N 4 %P 1181-8 %R 10.1111/j.1541-0420.2007.00814.x %T Evaluating the predictiveness of a continuous marker. %U http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3059154&tool=pmcentrez&rendertype=abstract %V 63 %X Consider a continuous marker for predicting a binary outcome. For example, the serum concentration of prostate specific antigen may be used to calculate the risk of finding prostate cancer in a biopsy. In this article, we argue that the predictive capacity of a marker has to do with the population distribution of risk given the marker and suggest a graphical tool, the predictiveness curve, that displays this distribution. The display provides a common meaningful scale for comparing markers that may not be comparable on their original scales. Some existing measures of predictiveness are shown to be summary indices derived from the predictiveness curve. We develop methods for making inference about the predictiveness curve, for making pointwise comparisons between two curves, and for evaluating covariate effects. Applications to risk prediction markers in cancer and cystic fibrosis are discussed. %@ 0006-341X

@article{Huang2007, abstract = {Consider a continuous marker for predicting a binary outcome. For example, the serum concentration of prostate specific antigen may be used to calculate the risk of finding prostate cancer in a biopsy. In this article, we argue that the predictive capacity of a marker has to do with the population distribution of risk given the marker and suggest a graphical tool, the predictiveness curve, that displays this distribution. The display provides a common meaningful scale for comparing markers that may not be comparable on their original scales. Some existing measures of predictiveness are shown to be summary indices derived from the predictiveness curve. We develop methods for making inference about the predictiveness curve, for making pointwise comparisons between two curves, and for evaluating covariate effects. Applications to risk prediction markers in cancer and cystic fibrosis are discussed.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Huang, Ying and Pepe, Margaret Sullivan and Feng, Ziding}, biburl = {https://www.bibsonomy.org/bibtex/2d479070afd61c43f217c2378d603e72e/jepcastel}, city = {University of Washington Biostatistics Department, F-600 Health Sciences Building, Box 357232, Seattle, Washington 98195-7232, USA.}, doi = {10.1111/j.1541-0420.2007.00814.x}, interhash = {e2b6ee4c73c7a16075bfc95eb01fef46}, intrahash = {d479070afd61c43f217c2378d603e72e}, isbn = {0006-341X}, issn = {0006-341X}, journal = {Biometrics}, keywords = {Algorithms BiologicalMarkers BiologicalMarkers:analysis Biometry Biometry:methods Computer-Assisted Computer-Assisted:methods ComputerSimulation DataInterpretation Diagnosis Models ReproducibilityofResults SensitivityandSpecificity Statistical}, month = {12}, note = {5174<br/>GR: R01 GM54438/GM/NIGMS NIH HHS/United States; GR: UO1 CA086368/CA/NCI NIH HHS/United States; JID: 0370625; 0 (Biological Markers); 2007/05/08 [aheadofprint]; ppublish;<br/>Models predictius; Proves diagnòstiques}, number = 4, pages = {1181-8}, pmid = {17489968}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Evaluating the predictiveness of a continuous marker.}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3059154&tool=pmcentrez&rendertype=abstract}, volume = 63, year = 2007 }