The DOI matches its Crossref record; 140 of the 141 references it cites were found in a registry or an index.
Net Reclassification Improvement: Computation, Interpretation, and Controversies: A Literature Review and Clinician's Guide
Open accessAbstract
The net reclassification improvement (NRI) is an increasingly popular measure for evaluating improvements in risk predictions. This article details a review of 67 publications in high-impact general clinical journals that considered the NRI. Incomplete reporting of NRI methods, incorrect calculation, and common misinterpretations were found. To aid improved applications of the NRI, the article elaborates on several aspects of the computation and interpretation in various settings. Limitations and controversies are discussed, including the effect of miscalibration of prediction models, the use of the continuous NRI and “clinical NRI,” and the relation with decision analytic measures. A systematic approach toward presenting NRI analysis is proposed: Detail and motivate the methods used for computation of the NRI, use clinically meaningful risk cutoffs for the category-based NRI, report both NRI components, address issues of calibration, and do not interpret the overall NRI as a percentage of the study population reclassified. Promising NRI findings need to be followed with decision analytic or formal cost-effectiveness evaluations.
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“Dedicated measures, such as the widely used but improper net reclassification improvement, are available for evaluating competing models.”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [119]
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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
Self-citationTraumatic brain injury: integrated approaches to improve prevention, clinical care, and research
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Metadata
- Authors
- Leening, M. J. G., Vedder, M. M., Witteman, J. C. M., Pencina, M. J., & Steyerberg, E. W.
- Affiliations
- Duke University (US)
- Venue
- Annals of Internal Medicine
- Publisher
- American College of Physicians
- Published
- 2014-01-21
- Volume
- 160
- Issue
- 2
- Pages
- 122-131
- ISSN
- 0003-4819 (ISSN-L)
- 1539-3704
- OpenAlex
- W2103645914
- Cited by
- 594
- References
- 141
- Citations by year
- 2026: 38 · 2025: 30 · 2024: 41 · 2023: 59 · 2022: 59 · 2021: 53 · 2020: 56 · 2019: 39 · 2018: 49 · 2017: 44 · 2016: 55 · 2015: 47 · 2014: 23
- FWCI
- 7.11
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Annals of Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- net reclassification improvement, risk prediction model, model calibration, cost-effectiveness, reporting quality
- MeSH terms
- Data Interpretation, Statistical, Decision Support Techniques, Humans, Models, Statistical, Risk Assessment
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