The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models
Open accessAbstract
Net reclassification and integrated discrimination improvements have been proposed as alternatives to the increase in the area under the curve for evaluating improvement in the performance of risk assessment algorithms introduced by the addition of new phenotypic or genetic markers. In this paper, we demonstrate that in the setting of linear discriminant analysis, under the assumptions of multivariate normality, all three measures can be presented as functions of the squared Mahalanobis distance. This relationship affords an interpretation of the magnitude of these measures in the familiar language of effect size for uncorrelated variables. Furthermore, it allows us to conclude that net reclassification improvement can be viewed as a universal measure of effect size. Our theoretical developments are illustrated with an example based on the Framingham Heart Study risk assessment model for high‐risk men in primary prevention of cardiovascular disease. Copyright © 2011 John Wiley & Sons, Ltd.
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Cites
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Self-citationDecision curve analysis: a novel method for evaluating prediction models
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Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147
Biomarker definitions and their applications
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Self-citationBendapudi, P. K., Hurwitz, S., Fry, A., Marques, M. B., Waldo, S. W., Li, A., Sun, L., Upadhyay, V., Hamdan, A., Brunner, A. M., et al. (2017) · The Lancet Haematology · doi:10.1016/s2352-3026(17)30026-1 · Cited by 561
Ohkuma, T., Ninomiya, T., Tomiyama, H., Kario, K., Hoshide, S., Kita, Y., Inoguchi, T., Maeda, Y., Kohara, K., Tabara, Y., et al. (2017) · Hypertension · doi:10.1161/hypertensionaha.117.09097 · Cited by 541
Interpreting Incremental Value of Markers Added to Risk Prediction Models
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Self-citationBonaca, M. P., Bhatt, D. L., Storey, R. F., Steg, P. G., Cohen, M., Kuder, J., Goodrich, E., Nicolau, J. C., Parkhomenko, A., López-Sendón, J., et al. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.03.524 · Cited by 389
Cardiovascular Disease Risk Assessment: Insights from Framingham
D’Agostino Sr., R. B., Pencina, M. J., Massaro, J. M., & Coady, S. (2013) · Global Heart · doi:10.1016/j.gheart.2013.01.001 · Cited by 329
Self-citationComparisons of established risk prediction models for cardiovascular disease: systematic review
Siontis, G. C. M., Tzoulaki, I., Siontis, K. C., & Ioannidis, J. P. A. (2012) · BMJ · doi:10.1136/bmj.e3318 · Cited by 300
Multivariable Modeling Strategies
Harrell, F. E. (2015) · Springer Series in Statistics · doi:10.1007/978-3-319-19425-7_4 · Cited by 221
Pepe, M. S., Fan, J., Feng, Z., Gerds, T., & Hilden, J. (2015) · Statistics in Biosciences · doi:10.1007/s12561-014-9118-0 · Cited by 191
“The continuous NRI statistic [12,13] is calculated as NRI=2{P[ ˆ risk(X,Y )> ˆ risk(X)|D = 1]−P[ ˆ risk(X,Y )> ˆ risk(X)|D = 0]} (1) the proportion of cases in the test dataset for whom ˆ risk(X,Y ) > ˆ risk(X) minus the corresponding proportion of controls, multiplied by 2.”
Singhal, A., Neveditsin, N., Tanveer, H., & Mago, V. (2024) · JMIR Medical Informatics · doi:10.2196/50048 · Cited by 172
Metadata
- Authors
- Pencina, M. J., D'Agostino, R. B., & Demler, O. V.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2011-12-07
- Volume
- 31
- Issue
- 2
- Pages
- 101-113
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W1990479835
- Cited by
- 349
- References
- 37
- FWCI
- 16.41
- Type
- journal article
- Language
- en
- Open access
- bronze
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