Cited by 657Meta-analysis and systematic reviewsStatistical Methods in EpidemiologyClinical practice guidelines implementation

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A guide to systematic review and meta-analysis of prediction model performance

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Debray, Thomas P A, Damen, Johanna A A G, Snell, Kym I E, Ensor, Joie, Hooft, Lotty, Reitsma, Johannes B, Riley, Richard D, Moons, Karel G M·2017·BMJ

DOI 10.1136/bmj.i6460

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Abstract

Validation of prediction models is highly recommended and increasingly common in the literature. A systematic review of validation studies is therefore helpful, with meta-analysis needed to summarise the predictive performance of the model being validated across different settings and populations. This article provides guidance for researchers systematically reviewing and meta-analysing the existing evidence on a specific prediction model, discusses good practice when quantitatively summarising the predictive performance of the model across studies, and provides recommendations for interpreting meta-analysis estimates of model performance. We present key steps of the meta-analysis and illustrate each step in an example review, by summarising the discrimination and calibration performance of the EuroSCORE for predicting operative mortality in patients undergoing coronary artery bypass grafting.

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References
Riley, R. D., Tierney, J. F., & Stewart, L. A. (2021) · Wiley eBooks · doi:10.1002/9781119333784.refs5
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References
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Authors: Debray, T. P. A., Damen, J. A. A. G., Snell, K. I. E., Ensor, J., Hooft, L., Reitsma, J. B., Riley, R. D., & Moons, K. G. M.
Affiliations: University Medical Center Utrecht (NL)
Keele University (GB)
Corresponding author: Debray, Thomas P A
ORCID: Debray, Thomas P A
Damen, Johanna A A G
Snell, Kym I E
Ensor, Joie
Hooft, Lotty
Reitsma, Johannes B
Riley, Richard D
Moons, Karel G M
Venue: BMJ
Publisher: BMJ
Published: 2017-01-05
Volume: 356
Pages: i6460
ISSN: 1756-1833 (ISSN-L)
DOI: 10.1136/bmj.i6460
OpenAlex: W2570878489
Cited by: 657
References: 48
Citations by year: 2026: 109 · 2025: 98 · 2024: 94 · 2023: 81 · 2022: 64 · 2021: 82 · 2020: 56 · 2019: 33 · 2018: 27 · 2017: 11
FWCI: 8.62
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.bmj.org/licenses/tdm/1.0/terms-and-conditions.html (tdm)
Locations: BMJ (publishedVersion)
PubMed (publishedVersion)
Pure Amsterdam UMC (publishedVersion)
Keele Research Repository (Keele University) (submittedVersion, cc-by)
Data Archiving and Networked Services (DANS) (submittedVersion)
Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
HighWire Press Open Archive (submittedVersion)
University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
Funders: Medical Research Council (MR/L004933/1, G0902393)
Topics: Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Clinical practice guidelines implementation
Keywords: prediction model validation, systematic reviews, meta-analysis, predictive performance, discrimination (statistics), calibration, EuroSCORE, postoperative mortality, coronary artery bypass grafting, risk prediction model
MeSH terms: Systematic Reviews as Topic, Coronary Artery Bypass, Decision Support Techniques, Humans, Meta-Analysis as Topic, Risk Assessment, Validation Studies as Topic

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A guide to systematic review and meta-analysis of prediction model performance

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