Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.
Appropriate quantification of added usefulness offered by new markers included in risk prediction algorithms is a problem of active research and debate. Standard methods, including statistical significance and c statistic are useful but not sufficient. Net reclassification improvement (NRI) offers a...
| Publicado en: | Statistics in Medicine Vol. 30; no. 1; pp. 11 - 22 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
Jan2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104983260&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104983260 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: Jan2011 vid: 30 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104983260 NLM21204120 2010905513 10.1002/sim.4085 NLM21204120 PMC3341973 104983260 ppf: 11 ppct: 11 formats: tig: atl: Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. aug: au: Pencina MJ D'Agostino RB Sr Steyerberg EW Pencina, Michael J D'Agostino, Ralph B Sr Steyerberg, Ewout W affil: Department of Biostatistics, Boston University, 801 Massachusetts Ave, Boston, MA 02118, USA sug: subj: Biological Markers Analysis Models, Biological Models, Statistical Risk Assessment Methods Adult Aged Case Control Studies Coronary Disease Epidemiology Coronary Disease Metabolism Female Human Kaplan-Meier Estimator Prospective Studies Male Middle Age Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years Female Male ab: Appropriate quantification of added usefulness offered by new markers included in risk prediction algorithms is a problem of active research and debate. Standard methods, including statistical significance and c statistic are useful but not sufficient. Net reclassification improvement (NRI) offers a simple intuitive way of quantifying improvement offered by new markers and has been gaining popularity among researchers. However, several aspects of the NRI have not been studied in sufficient detail. In this paper we propose a prospective formulation for the NRI which offers immediate application to survival and competing risk data as well as allows for easy weighting with observed or perceived costs. We address the issue of the number and choice of categories and their impact on NRI. We contrast category-based NRI with one which is category-free and conclude that NRIs cannot be compared across studies unless they are defined in the same manner. We discuss the impact of differing event rates when models are applied to different samples or definitions of events and durations of follow-up vary between studies. We also show how NRI can be applied to case-control data. The concepts presented in the paper are illustrated in a Framingham Heart Study example. In conclusion, NRI can be readily calculated for survival, competing risk, and case-control data, is more objective and comparable across studies using the category-free version, and can include relative costs for classifications. We recommend that researchers clearly define and justify the choices they make when choosing NRI for their application. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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