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

By Gary S. Collins, Johannes B Reitsma, DG Altman, Karel G. M. Moons. 2015

894 citations recorded by the source corpus.

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).

Subfields: Economics and Econometrics, Family Practice, Statistics and Probability, Medical & Health AI

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Citations in the corpus

Model Cards for Model Reporting (2018, background): “Model cards are also similar to the tripod statement proposal in medicine [25].”
STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. (2015, background): “Other reporting guidelines target more specific forms of tests, such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) for multivariable prediction models (14).”
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology (2018, methodology): “Similarly, studies reporting predictive models should be compliant with the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement [21], which emphasizes the transparent reporting of study settings, outcome follow-up intervals, or precise d”
Assessment of Clinical Criteria for Sepsis (2016, neutral): “(%) 17 327 (12) 2153 (27) 6517 (10) 2171 (27) 6486 (10)”
Sample size for binary logistic prediction models: Beyond events per variable criteria (2018, background): “Two major categories are: diagnostic prediction models that estimate the probability of a target disease being currently present versus not present; and prognostic prediction models that predict the probability of developing a certain health state or disease outcome over a certain time period.(3) Th”
Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. (2020, methodology): “We created a new section at the end of the checklist pertaining to administrative information, such as funding, which mirrors the style used in other reporting guidelines (15, 17, 46, 47).”
SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. (2021, methodology): “We adopted analytical approaches and reporting standards recommended by the PROBAST guidelines(33) and TRIPOD.(34) Analyses were performed with R-statistic programming (version 3.”
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study (2019, neutral): “Our studycomplieswith theTRIPODstatement[45] asdeclaredin S7Table.”
A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. (2018, result): “The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) Statement is a guideline specifically designed for the reporting of studies developing or validating a multivariable prediction model (65).”