Cited by 1,927Meta-analysis and systematic reviewsStatistical Methods in EpidemiologyClinical practice guidelines implementation

The DOI matches its Crossref record; 70 of the 71 references it cites were found in a registry or an index; 1 of the 40 sources its claims cite were checked for support.

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement

Open access
Collins, Gary S, Reitsma, Johannes B, Altman, Douglas G, Moons, Karel·2015·BMC Medicine

Abstract

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org ). Editors’ note: In order to encourage dissemination of the TRIPOD Statement, this article is freely accessible on the Annals of Internal Medicine Web site ( www.annals.org ) and will be also published in BJOG, British Journal of Cancer, British Journal of Surgery, BMC Medicine, British Medical Journal, Circulation, Diabetic Medicine, European Journal of Clinical Investigation, European Urology, and Journal of Clinical Epidemiology. The authors jointly hold the copyright of this article. An accompanying Explanation and Elaboration article is freely available only on www.annals.org ; Annals of Internal Medicine holds copyright for that article.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

The TRIPOD Statement is a reporting guideline consisting of a 22-item checklist for studies developing, validating, or updating multivariable prediction models for diagnosis or prognosis. It was developed through literature review, a Web-based survey, and a consensus meeting to address the generally poor reporting quality of prediction model studies.

Key findings

  • The TRIPOD Statement is a checklist of 22 items deemed essential for transparent reporting of prediction model studies, covering title and abstract, background and objectives, methods, results, discussion, and other information.
  • Items are marked as relevant to development (D), validation (V), or both (D;V); development-only items include 10a, 10b, 14, and 15, while validation-only items include 10c, 10e, 12, 13c, 17, and 19a.
  • Cited reviews found poor reporting: Mallett and colleagues examined 47 reports of new cancer prediction models; Collins and colleagues evaluated 39 reports of type 2 diabetes prediction models; Bouwmeester and colleagues evaluated 71 reports published in 2008 in 6 high-impact general medical journals.
  • TRIPOD is not intended for multivariable modeling in etiologic studies, studies investigating single prognostic factors, or impact studies quantifying the impact of using a prediction model.

Methods

  • Guideline development followed published guidance for developing reporting guidelines and established a steering committee; a systematic search of MEDLINE, EMBASE, PsychINFO, and Web of Science generated 129 possible checklist items, which were merged into 76 candidate items.
  • A Web-based survey rated the 76 candidate items; a 3-day meeting in June 2011 in Oxford, United Kingdom, was attended by 24 of 25 invited participants, 22 of whom had participated in the survey.
  • The checklist was revised by the steering committee and circulated to participants; the final TRIPOD Statement is a 22-item checklist, accompanied by an explanation and elaboration document.

Limitations

  • The paper states that TRIPOD is guidelines only for reporting and does not prescribe how to develop or validate a prediction model, nor is it a quality assessment tool to gauge model quality or quantify risk of bias.
  • The paper states that methodological issues in developing, validating, and updating prediction models evolve, and TRIPOD will be periodically reappraised and modified if necessary.
  • Evident from the text: the paper does not report an empirical evaluation of whether the TRIPOD checklist itself improves reporting quality or clinical use; it presents the development process and the checklist.

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Integrity signals

Registry

Crossref · 10.1186/s12916-014-0241-z

Cites

70 of 70 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Figure 1 Schematic representation of diagnostic and prognostic prediction modeling studies. The nature of the prediction in diagnosis is estimating the probability that a specific outcome or disease is present (or absent) within an individual, at this point in time—that is, the moment of prediction (T = 0). In prognosis, the prediction is about whether an individual will experience a specific event or outcome within a certain time period. In other words, in diagnostic prediction the interest is in principle a cross-sectional relationship, whereas prognostic prediction involves a longitudinal relationship. Nevertheless, in diagnostic modeling studies, for logistical reasons, a time window between predictor (index test) measurement and the reference standard is often necessary. Ideally, this interval should be as short as possible and without starting any treatment within this period.
Figure 1 Schematic representation of diagnostic and prognostic prediction modeling studies. The nature of the prediction in diagnosis is estimating the probability that a specific outcome or disease is present (or absent) within an individual, at this point in time—that is, the moment of prediction (T = 0). In prognosis, the prediction is about whether an individual will experience a specific event or outcome within a certain time period. In other words, in diagnostic prediction the interest is in principle a cross-sectional relationship, whereas prognostic prediction involves a longitudinal relationship. Nevertheless, in diagnostic modeling studies, for logistical reasons, a time window between predictor (index test) measurement and the reference standard is often necessary. Ideally, this interval should be as short as possible and without starting any treatment within this period.CC BY · PubMed Central
Figure 2 Similarities and differences between diagnostic and prognostic prediction models.
Figure 2 Similarities and differences between diagnostic and prognostic prediction models.CC BY · PubMed Central
Figure 3 Types of prediction model studies covered by the TRIPOD statement. D = development data; V = validation data.
Figure 3 Types of prediction model studies covered by the TRIPOD statement. D = development data; V = validation data.CC BY · PubMed Central

Verification

Claims the paper makes

19 Not checked yet · 8 Own interpretation · 6 Own method · 2 No basis given · 1 Supported so far

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  • Probably not a substantial claim. 21
  • It may state two separate claims as one. 10
23 of 23
  • The paper relies on this without support.

    Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed.

    “Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed.”
  • The paper relies on this without support.

    The consensus process and expert opinion used to develop TRIPOD yield checklist items that are essential for transparent reporting of a prediction model study.

    “The TRIPOD Statement is a checklist of 22 items that we consider essential for good reporting of studies developing or validating multivariable prediction models (Table 1).”
  • The paper relies on this without support.

    The 25 invited experts and meeting participants are representative of the relevant expertise for developing a reporting guideline for prediction models.

    “Twenty-five experts with a specific interest in prediction models were invited by e-mail to participate in the Web-based survey and to rate the importance of the 76 candidate checklist items.”
  • The paper relies on this without support.

    The literature search and item reduction process adequately captured all relevant reporting recommendations for prediction models.

    “From these studies, a list of 129 possible checklist items was generated. The steering committee then merged related items to create a list of 76 candidate items.”
4 of 4

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Citations

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Metadata

Authors
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K.
Affiliations
  • University Medical Center Utrecht (NL)
  • University of Oxford (GB)
Venue
BMC Medicine
Published
2015
Volume
13
Issue
1
Pages
1
ISSN
1741-7015 (ISSN-L)
OpenAlex
W2019694480
Cited by
1,927
References
71
Citations by year
2026: 213 · 2025: 303 · 2024: 330 · 2023: 295 · 2022: 236 · 2021: 178 · 2020: 110 · 2019: 101 · 2018: 50 · 2017: 60 · 2016: 42 · 2015: 6
FWCI
47.98
Type
journal article
Language
en
Open access
Locations
  • BMC Medicine (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • University of Birmingham Research Portal (University of Birmingham) (acceptedVersion, cc-by)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
Funders
  • Cancer Research UK (16895, C5529)
  • National Institute for Health and Care Research (NF-SI-0508-10259, NF-SI-0513-10131)
  • ZonMw (91208004)
  • Nederlandse Organisatie voor Wetenschappelijk Onderzoek (918.10.615)
  • Medical Research Council
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
TRIPOD statement, multivariable prediction models, reporting quality, diagnostic model, prognostic model, reporting checklist, risk of bias, prediction model validation, clinical decision-making
MeSH terms
Diagnosis, Humans, Models, Theoretical, Prognosis, Multivariate Analysis, Guidelines as Topic, Risk Assessment, Checklist

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