Skip to main content
Journal cover image

Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.

Publication ,  Journal Article
Pencina, MJ; D'Agostino, RB; Steyerberg, EW
Published in: Stat Med
January 15, 2011

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.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

January 15, 2011

Volume

30

Issue

1

Start / End Page

11 / 21

Location

England

Related Subject Headings

  • Statistics & Probability
  • Risk Assessment
  • Models, Statistical
  • Models, Biological
  • Middle Aged
  • Male
  • Longitudinal Studies
  • Kaplan-Meier Estimate
  • Humans
  • Female
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Pencina, M. J., D’Agostino, R. B., & Steyerberg, E. W. (2011). Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med, 30(1), 11–21. https://doi.org/10.1002/sim.4085
Pencina, Michael J., Ralph B. D’Agostino, and Ewout W. Steyerberg. “Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.Stat Med 30, no. 1 (January 15, 2011): 11–21. https://doi.org/10.1002/sim.4085.
Pencina MJ, D’Agostino RB, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011 Jan 15;30(1):11–21.
Pencina, Michael J., et al. “Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.Stat Med, vol. 30, no. 1, Jan. 2011, pp. 11–21. Pubmed, doi:10.1002/sim.4085.
Pencina MJ, D’Agostino RB, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011 Jan 15;30(1):11–21.
Journal cover image

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

January 15, 2011

Volume

30

Issue

1

Start / End Page

11 / 21

Location

England

Related Subject Headings

  • Statistics & Probability
  • Risk Assessment
  • Models, Statistical
  • Models, Biological
  • Middle Aged
  • Male
  • Longitudinal Studies
  • Kaplan-Meier Estimate
  • Humans
  • Female