Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement
Open accessAbstract
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).
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- Findingcitation
TRIPOD excludes impact studies that measure how using a model affects behavior, health outcomes, or cost-effectiveness.
“Furthermore, TRIPOD is also not intended for impact studies that quantify the impact of using a prediction model on participant or doctors' behavior and management, participant health outcomes, or cost-effectiveness of care, compared with not using the model (13,38).”
Quote in the paperSubstantial claim0 of 2 sources checked - Premisecitation
Probability estimates in diagnostic and prognostic contexts seldom rest on one predictor alone.
“In both the diagnostic and prognostic setting, estimates of probabilities are rarely based on a single pre-dictor (5).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] source
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- Premisecitation
A substantial number of prediction model studies have weak methodology.
“because many such studies are methodologically weak, we also summarize the qualities of good (and the limitations of less good) studies, regardless of reporting (43).”
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- Premisecitation
Overfitting, optimism, and miscalibration can be addressed during model development by shrinkage or penalization.
“Overfitting, optimism, and miscalibration may also be addressed and accounted for during the model development by applying shrinkage (for example, heuristic or based on bootstrapping techniques) or penalization procedures (for example, ridge regression or lasso) (20).”
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- Premisecitation
Very few prediction models, relative to the large number published, are widely implemented or used in clinical practice.
“very few prediction models, relative to the large number of models published, are widely implemented or used in clinical practice (6).”
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- Premisecitation
When key study details are not reported in detail and transparently, it is hard for the scientific and health care community to judge objectively the strengths and weaknesses of a prediction model study.
“In the absence of detailed and transparent reporting of the key study details, it is difficult for the scientific and health care community to objectively judge the strengths and weaknesses of a prediction model study (34,50,51).”
Quote in the paperSubstantial claim0 of 3 sources checked - Premisecitation
Multiple reviews have found that published articles on the development or validation of multivariable prediction models in medicine report their methods poorly.
“Many reviews have showed that the quality of reporting in published articles describing the development or validation of multivariable prediction models in medicine is poor (23)(24)(25)(26)(27)29).”
Quote in the paperSubstantial claim0 of 3 sources checked - Premisecitation
Quantifying predictive ability on the same data used for development tends to give an optimistic estimate of performance due to overfitting and predictor selection.
“Quantifying the predictive ability of a model on the same data from which the model was developed (often referred to as apparent performance) will tend to give an optimistic estimate of performance, owing to overfitting (too few outcome events relative to the number of candidate predictors) and the use of predictor selection strategies (19).”
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- Premisecitation
The reporting quality of studies that develop or validate multivariable prediction models is generally poor across disease areas and journals.
“However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor.”
- Also stated in: § REPORTING OF MULTIVARIABLE PREDICTION MODEL STUDIES · boosted
“These and other reviews provide a clear picture that, across different disease areas and different journals, there is a generally poor level of reporting of prediction model studies (6,(23)(24)(25)(26)(27)29).”
Quote in the paperSubstantial claim0 of 3 sources checked - Also stated in: § REPORTING OF MULTIVARIABLE PREDICTION MODEL STUDIES · boosted
- May combine two claims. 13
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- The quote only partly states this claim. 1
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FindingcitationTRIPOD does not cover etiologic multivariable modeling or studies of single prognostic factors.
“TRIPOD is not intended for multivariable modeling in etiologic studies or for studies investigating single prognostic factors (37).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [37] source
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FindingcitationThe accompanying explanation and elaboration document describes every checklist item and gives examples of good reporting.
“Each checklist item is explained and accompanied by examples of good reporting from published articles.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [43] source
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Findingnone givenTRIPOD gives greater weight to validation studies and what must be reported for them.
“TRIPOD also places much more emphasis on validation studies and the reporting requirements for such studies.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
FindingreasoningTRIPOD is not meant to serve as an instrument for judging the quality of a prediction model.
“Furthermore, the checklist is not a quality assessment tool to gauge the quality of a multivariable prediction model.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
FindingreasoningTRIPOD offers reporting guidance only; it does not dictate how a prediction model should be derived or validated.
“The recommendations within TRIPOD are guidelines only for reporting research and do not prescribe how to develop or validate a prediction model.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
FindingcitationStudies that assess whether a new predictor adds value over known predictors or existing models fall within TRIPOD's scope.
“The reporting of studies evaluating the incremental value of specific predictors, beyond established predictors or even beyond existing prediction models (18,42), also fits entirely within the remit of TRIPOD”
Quote in the paperNot a substantial claim0 of 2 sources checkedClassified as probably not a substantial claim.
FindingreasoningTRIPOD consists of 22 checklist items that its authors regard as necessary for adequate reporting of prediction model derivation or validation studies.
“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).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
FindingcitationThe E&E document also reviews what makes studies good and what limits weaker studies, independent of reporting quality.
“we also summarize the qualities of good (and the limitations of less good) studies, regardless of reporting (43).”
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PremisecitationModel development requires internal validation.
“Internal validation is a necessary part of model development (2).”
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Study design and conductreasoningWhere possible, TRIPOD's recommendations were aligned with other reporting guidelines.
“While making revisions, conscious efforts were made to harmonize our recommendations with other reporting guidelines, and where possible we chose the same or similar wording for items (37,39,41,45,46).”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationClinical practice guidelines increasingly advise using prediction models, according to health care providers and policy makers.
“Health care providers and policy makers are increasingly recommending the use of prediction models within clinical practice guidelines to inform decision making at various stages in the clinical pathway (30,31).”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningExisting systematic reviews provided an extensive evidence base to justify the inclusion and illustration of each checklist item.
“A comprehensive evidence base from existing systematic reviews of prediction models was used to support and justify the rationale for including and illustrating each checklist item.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningPrediction is inherently multivariable.
“Prediction is therefore inherently multivariable.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningClear reporting of the essential steps used to derive and validate a prediction model is necessary for synthesis and critical appraisal.
“It is therefore essential that key details of how a prediction model was developed and validated be clearly reported to enable synthesis and critical appraisal of all relevant information (14,(33)(34)(35)(36).”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
PremisereasoningAlthough STROBE, REMARK, STARD, and GRIPS include many items relevant to prediction model studies, no existing guideline is fully suitable for them.
“studies all contain many items that are relevant to studies developing or validating prediction models.However, none of these guidelines are entirely appropriate for prediction model studies.”
Quote in the paperSubstantial claim0 of 3 sources checkedMay combine two claims.
PremisecitationReviews have commonly found serious deficiencies in statistical methods, small datasets, missing data handling, and lack of validation.
“these reviews have shown that serious deficiencies in the statistical methods, use of small data sets, inappropriate handling of missing data, and lack of validation are common (6,(23)(24)(25)(26)(27)29).”
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Study design and conductown dataAt the three-day meeting, all 76 candidate items were discussed one by one, and the group agreed whether to keep, combine, or drop each.
“During the 3-day meeting, each of the 76 candidate checklist items was discussed in turn, and a consensus was reached on whether to retain, merge with another item, or omit the item.”
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PremisereasoningDiagnostic and prognostic prediction models share the same challenges, including predictor selection, model-building strategies, handling continuous predictors, and overfitting.
“The same challenges as when developing a multivariable prediction model, such as selection of the predictors, model-building strategies, and handling of continuous predictors and the danger of overfitting.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims. The quote only partly states this claim.
Study design and conductreasoningDevelopment of TRIPOD followed published guidance for reporting guidelines and involved a systematic literature search, an online expert survey, and a three-day consensus meeting.
“We followed published guidance for developing reporting guidelines (44) and established a steering committee (Drs.Collins, Reitsma, Altman, and Moons) to organize and coordinate the development of TRIPOD.”
- Also stated in: § DEVELOPING THE TRIPOD STATEMENT · plain
“The results of the survey were presented at a 3-day meeting in June 2011, in Oxford, United Kingdom; it was attended by 24 of the 25 invited participants (22 of whom had participated in the survey).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [44] source
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- Also stated in: § DEVELOPING THE TRIPOD STATEMENT · plain
May combine two claims.
PremisecitationREMARK focuses primarily on prognostic factors rather than prediction models, and GRIPS targets genetic risk prediction and issues with many genetic variants.
“the focus of the REMARK checklist is primarily on prognostic factors and not prediction models, whereas the GRIPS statement is aimed at risk prediction using genetic risk factors and the specific methodological issues around handling large numbers of genetic variants.”
Quote in the paperSubstantial claim0 of 2 sources checkedMay combine two claims.
InterpretationreasoningThese methodological problems result in models that either cannot be used or ought not to be used.
“Such deficiencies ultimately lead to prediction models that are not or should not be used.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenDiagnostic and prognostic prediction models use the same measures for assessing model performance.
“The same measures for assessing model performance.”
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Study design and conductnone givenThe funding sources played no role in the study design, data collection, analysis, manuscript preparation, or the decision to submit the manuscript for publication.
“The funding sources had no role in the study design, data collection, analysis, preparation of the manuscript, or decision to submit the manuscript for publication.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductreasoningAfter the meeting, the steering committee revised the checklist in multiple face-to-face meetings and sent it to participants so that it matched their discussions.
“After the meeting, the checklist was revised by the steering committee during numerous face-to-face meetings, and circulated to the participants to ensure it reflected the discussions.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationResearch reporting generally requires that others be able to repeat every step and get identical results.
“It is a general requirement of reporting of research that other researchers can, if required, replicate all the steps taken and obtain the same results (32).”
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InterpretationreasoningAdequate assessment of a prediction model's risk of bias and potential usefulness requires complete and clear reporting of every aspect of the model.
“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.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisereasoningDiagnostic prediction concerns a cross-sectional relationship, while prognostic prediction concerns a longitudinal relationship.
“in diagnostic prediction the interest is in principle a cross-sectional relationship, whereas prognostic prediction involves a longitudinal relationship.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
The number of prediction models in medicine has grown rapidly in recent years.
“Prediction models in medicine have proliferated in recent years.”
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Reporting and evaluation frameworks aim to improve transparency and assess methodological quality in prediction models.
“Reporting and evaluation frameworks aim to improve transparency and assess methodological quality in prediction models [25,26].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [26]
The Discussion says TRIPOD aims to improve reporting and help readers critically appraise prediction-model reports. It also explicitly says TRIPOD should not be used to assess study quality or quantify risk of bias, so the methodological-quality assessment part is not supported for this framework. (LLM, conf=0.88)
Experimental — accuracy not yet measuredExisting nomograms demonstrate moderate discrimination (c-statistic 0.70-0.85) but suffer from overfitting, poor calibration in external cohorts, and reliance on imaging or specialized metrics unavailable in routine practice.
“Existing nomograms demonstrate moderate discrimination (c-statistic 0.70–0.85) but suffer from overfitting, poor calibration in external cohorts, and reliance on imaging or specialized metrics unavailable in routine practice”
From Individualized Prediction of Recurrence Following Uterine-Preserving Pelvic Organ Prolapse Repair Using Internally Validated Machine Learning Models (2026) · cites this paper as [11]
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“Bouwmeester and colleagues [23] evaluated 71 reports, published in 2008 in 6 high-impact general medical journals, and likewise observed an overwhelmingly poor level of reporting.”
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Self-citationReporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration
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“For example, Mallett and colleagues [26] examined 47 reports published in 2005 presenting new prediction models in cancer.”
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Self-citationRegistration of observational studies: Is it time?
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Fracture Risk Assessment: State of the Art, Methodologically Unsound, or Poorly Reported?
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Self-citationDeveloping and Evaluating Prediction Models in Rehabilitation Populations
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Conflicts at the heart of the FRAX tool
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Self-citationReporting of artificial intelligence prediction models
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Self-citationDevelopment of a novel frailty index to predict mortality in patients with end‐stage liver disease
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Early detection of type 2 diabetes mellitus using machine learning-based prediction models
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“…bootstrapping) or robust external validation on independent multi-institutional datasets, employing discrimination (AUC-ROC, sensitivity, specificity), calibration (Hosmer–Lemeshow test), and clinical utility metrics (decision curve analysis), adhering to RQS and TRIPOD guidelines [83,84].”
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“Reporting and evaluation frameworks aim to improve transparency and assess methodological quality in prediction models [25,26].”
Malul, S., Chill, H. H., Yosef, R., Miron-Shatz, T., & Shveiky, D. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15166366
- Authors
- Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M.
- Affiliations
- From Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, United Kingdom, and Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.
- University Medical Center Utrecht (NL)
- Venue
- Annals of Internal Medicine
- Publisher
- American College of Physicians
- Published
- 2015-01-06
- Volume
- 162
- Issue
- 1
- Pages
- 55-63
- ISSN
- 0003-4819 (ISSN-L)
- 1539-3704
- OpenAlex
- W2078271269
- Semantic Scholar
- 1dea74b8cc99f2cbf6756496aad0bab54891f5f1
- Cited by
- 3,267
- References
- 69
- Citations by year
- 2026: 736 · 2025: 292 · 2024: 342 · 2023: 356 · 2022: 326 · 2021: 341 · 2020: 268 · 2019: 187 · 2018: 155 · 2017: 123 · 2016: 81 · 2015: 59 · 2012: 1
- FWCI
- 125.67
- Type
- journal article
- Language
- en
- Open access
- green
- dspace.library.uu.nl
- Licence
- other-oa
- Locations
- Annals of Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- University of Birmingham Research Portal (University of Birmingham) (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Funders
- Vanderbilt University
- Memorial Sloan-Kettering Cancer Center
- Cleveland Clinic
- University of Ottawa
- Cancer Research UK (16895, C5529)
- National Institute for Health and Care Research (NF-SI-0508-10259, NF-SI-0513-10131)
- University College London
- University of Oxford
- Universiteit Maastricht
- Albert-Ludwigs-Universität Freiburg
- University of North Carolina at Chapel Hill
- 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, prognostic model, diagnostic model, reporting quality, reporting checklist, risk of bias, prediction model validation, clinical decision-making
- MeSH terms
- Decision Support Techniques, Diagnosis, Humans, Prognosis, Publishing, Models, Statistical, Multivariate Analysis, Checklist
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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