BACKGROUND: Prediction models are developed to aid healthcare 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. MATERIALS AND METHODS: 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, healthcare 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. RESULTS: 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. CONCLUSIONS: 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).
BACKGROUND: Prediction models are developed to aid healthcare 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. MATERIALS AND METHODS: 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, healthcare 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. RESULTS: 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. CONCLUSIONS: 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).
Authors: Ibrahim Chamseddine; Yejin Kim; Brian De; Issam El Naqa; Dan G Duda; John Wolfgang; Jennifer Pursley; Harald Paganetti; Jennifer Wo; Theodore Hong; Eugene J Koay; Clemens Grassberger Journal: JCO Clin Cancer Inform Date: 2022-02
Authors: Thomas C Booth; Gerard Thompson; Helen Bulbeck; Florien Boele; Craig Buckley; Jorge Cardoso; Liane Dos Santos Canas; David Jenkinson; Keyoumars Ashkan; Jack Kreindler; Nicky Huskens; Aysha Luis; Catherine McBain; Samantha J Mills; Marc Modat; Nick Morley; Caroline Murphy; Sebastian Ourselin; Mark Pennington; James Powell; David Summers; Adam D Waldman; Colin Watts; Matthew Williams; Robin Grant; Michael D Jenkinson Journal: Front Oncol Date: 2021-02-09 Impact factor: 6.244
Authors: Marta Ferreira; Pierre Lovinfosse; Johanne Hermesse; Marjolein Decuypere; Caroline Rousseau; François Lucia; Ulrike Schick; Caroline Reinhold; Philippe Robin; Mathieu Hatt; Dimitris Visvikis; Claire Bernard; Ralph T H Leijenaar; Frédéric Kridelka; Philippe Lambin; Patrick E Meyer; Roland Hustinx Journal: Eur J Nucl Med Mol Imaging Date: 2021-03-26 Impact factor: 9.236