|Title||Multicategory reclassification statistics for assessing improvements in diagnostic accuracy.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Li, Jialiang, Binyan Jiang, and Jason P. Fine|
|Date Published||2013 Apr|
|Keywords||Area Under Curve, Biomarkers, Biostatistics, Computer Simulation, Diagnostic Tests, Routine, Gene Expression, Humans, Leukemia, Logistic Models, Models, Statistical, ROC Curve, Synovitis|
In this paper, we extend the definitions of the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) in the context of multicategory classification. Both measures were proposed in Pencina and others (2008. Evaluating the added predictive ability of a new marker: from area under the receiver operating characteristic (ROC) curve to reclassification and beyond. Statistics in Medicine 27, 157-172) as numeric characterizations of accuracy improvement for binary diagnostic tests and were shown to have certain advantage over analyses based on ROC curves or other regression approaches. Estimation and inference procedures for the multiclass NRI and IDI are provided in this paper along with necessary asymptotic distributional results. Simulations are conducted to study the finite-sample properties of the proposed estimators. Two medical examples are considered to illustrate our methodology.
|Original Publication||Multicategory reclassification statistics for assessing improvements in diagnostic accuracy.|
|PubMed Central ID||PMC3695653|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
P30 AI050410 / AI / NIAID NIH HHS / United States
R01 CA094893 / CA / NCI NIH HHS / United States
Multicategory reclassification statistics for assessing improvements in diagnostic accuracy.