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A guide to systematic review and meta-analysis of prediction model performance
Open accessAbstract
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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“These methods mainly focused on (diagnostic) prediction models with a binary outcome, and may therefore have limited value when reviewing (prognostic) models with a time-to-event outcome.”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [14]
In the citing paper
“In particular, the total O:E ratio quantifies the averaged observed:expected ratio across the entire range of predicted risks, and is strongly related to calibration-in-the-large.14”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [14]
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“In particular, discrimination and calibration performance are highly dependent on patient spectrum (case-mix variation) and therefore most likely to vary across validation studies.14”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [14]
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“However, it is common for validation studies to present information on the total number of observed (O) and expected (E) events, or the corresponding observed frequencies PO and PE. These quantities can then be used to calculate the total O:E ratio”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [14]
In the citing paper
“Quantitative synthesis of prediction model studies may help to better understand their potential generalizability and can be achieved by applying meta-analysis methods.14”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [14]
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Cites
Two new Later Stone Age sites from the Final Pleistocene in the Falémé Valley, eastern Senegal
Ndiaye, M., Lespez, L., Tribolo, C., Rassé, M., Hadjas, I., Davidoux, S., Huysecom, É., & Douze, K. (2024) · Monash University Research Portal (Monash University) · doi:10.3929/ethz-b-000667478 · Cited by 24,214
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Estimating the mean and variance from the median, range, and the size of a sample
Hozo, S. P., Djulbegovic, B., & Hozo, I. (2005) · BMC Medical Research Methodology · doi:10.1186/1471-2288-5-13 · Cited by 9,062
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-citationCollins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0697 · Cited by 3,267
Self-citationInterpretation of random effects meta-analyses
Riley, R. D., Higgins, J. P. T., & Deeks, J. J. (2011) · BMJ · doi:10.1136/bmj.d549 · Cited by 2,884
Self-citationChapter 9: Analysing Data and Undertaking Meta-Analyses
Deeks, J. J., Higgins, J. P. T., & Altman, D. G. (2008) · Cited by 2,782
IntHout, J., Ioannidis, J. P., & Borm, G. F. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-25 · Cited by 2,419
Stewart, L. A., Clarke, M., Rovers, M., Riley, R. D., Simmonds, M., Stewart, G., & Tierney, J. F. (2015) · JAMA · doi:10.1001/jama.2015.3656 · Cited by 2,134
Self-citationMoons, K. G. M., de Groot, J. A. H., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., & Collins, G. S. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001744 · Cited by 2,018
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PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & for the PROBAST Group† (2019) · Annals of Internal Medicine · doi:10.7326/m18-1376 · Cited by 2,984
Self-citationCalculating the sample size required for developing a clinical prediction model
Riley, R. D., Ensor, J., Snell, K. I. E., Harrell, F. E., Martin, G. P., Reitsma, J. B., Moons, K. G. M., Collins, G., & van Smeden, M. (2020) · BMJ · doi:10.1136/bmj.m441 · Cited by 2,917
Self-citationMoons, K. G. M., Wolff, R. F., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., & Mallett, S. (2019) · Annals of Internal Medicine · doi:10.7326/m18-1377 · Cited by 1,669
Self-citationRiley, R. D., Snell, K. I., Ensor, J., Burke, D. L., Harrell Jr, F. E., Moons, K. G., & Collins, G. S. (2019) · Statistics in Medicine · doi:10.1002/sim.7992 · Cited by 1,409
Self-citationMoons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A. L., Van Calster, B., Celi, L. A., Denaxas, S., et al. (2025) · BMJ · doi:10.1136/bmj-2024-082505 · Cited by 1,069
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Metadata
- 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
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2017-01-05
- Volume
- 356
- Pages
- i6460
- ISSN
- 1756-1833 (ISSN-L)
- 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
- 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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