Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

By Gary S. Collins, Johannes B Reitsma, Douglas G. Altman, Karel G. M. Moons. Journal of Clinical Epidemiology, 2015

1,177 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

View Paper: https://www.semanticscholar.org/paper/9d469cecf2fede64643c9fa94faa4475e386e88d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Gary S. Collins
2. Ewout W. Steyerberg
3. B. Van calster
4. Nektarios A. Valous
5. Karel G. M. Moons
6. Douglas G. Altman
7. Johannes B Reitsma

In Family Practice

1. Gary S. Collins

In Statistics and Probability

1. Gary S. Collins
2. B. Van calster
3. Ewout W. Steyerberg
4. Evangelia Christodoulou
5. J. Verbakel
6. Jie Ma
7. Andre L. A. J. Dekker
8. I. E. El Naqa

Citations in the corpus

• Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries (2017, positive): “In this study, we conducted a level III and IV modeling effort on historical data, as a proof of concept, conforming with the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) statement, inwhich validation is performed at an independent site tha”
• A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. (2019, methodology): “Secondary objectives were to describe the characteristics of the studies, the type of ML algorithms that were used, the validation process, the modeling aspects of LR and ML, reporting quality, and risk of bias for comparing performance be-tween regression and ML [19].”
• Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study (2019, positive): “Our study complies with the TRIPOD statement [45] as declared in S7 Table.”