Literature DB >> 25560714

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

Gary S Collins, Johannes B Reitsma, Douglas G Altman, Karel G M Moons.   

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).

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Year:  2015        PMID: 25560714     DOI: 10.7326/M14-0697

Source DB:  PubMed          Journal:  Ann Intern Med        ISSN: 0003-4819            Impact factor:   25.391


  614 in total

Review 1.  Statistical considerations on prognostic models for glioma.

Authors:  Annette M Molinaro; Margaret R Wrensch; Robert B Jenkins; Jeanette E Eckel-Passow
Journal:  Neuro Oncol       Date:  2015-12-08       Impact factor: 12.300

2.  Refining Prognosis in Lung Cancer: A Report on the Quality and Relevance of Clinical Prognostic Tools.

Authors:  Alyson L Mahar; Carolyn Compton; Lisa M McShane; Susan Halabi; Hisao Asamura; Ramon Rami-Porta; Patti A Groome
Journal:  J Thorac Oncol       Date:  2015-11       Impact factor: 15.609

3.  An Official American Thoracic Society Research Statement: A Research Framework for Pulmonary Nodule Evaluation and Management.

Authors:  Christopher G Slatore; Nanda Horeweg; James R Jett; David E Midthun; Charles A Powell; Renda Soylemez Wiener; Juan P Wisnivesky; Michael K Gould
Journal:  Am J Respir Crit Care Med       Date:  2015-08-15       Impact factor: 21.405

4.  Fractional excretion of urea: A simple tool for the differential diagnosis of acute kidney injury in cirrhosis.

Authors:  Kavish R Patidar; Le Kang; Jasmohan S Bajaj; Daniel Carl; Arun J Sanyal
Journal:  Hepatology       Date:  2018-05-17       Impact factor: 17.425

5.  Development and Validation of Objective and Quantitative Eye Tracking-Based Measures of Autism Risk and Symptom Levels.

Authors:  Thomas W Frazier; Eric W Klingemier; Sumit Parikh; Leslie Speer; Mark S Strauss; Charis Eng; Antonio Y Hardan; Eric A Youngstrom
Journal:  J Am Acad Child Adolesc Psychiatry       Date:  2018-09-13       Impact factor: 8.829

6.  External validation and comparison of multiple prognostic scores in allogeneic hematopoietic stem cell transplantation.

Authors:  Roni Shouval; Joshua A Fein; Aniela Shouval; Ivetta Danylesko; Noga Shem-Tov; Maya Zlotnik; Ronit Yerushalmi; Avichai Shimoni; Arnon Nagler
Journal:  Blood Adv       Date:  2019-06-25

7.  A novel protein-based prognostic signature improves risk stratification to guide clinical management in early-stage lung adenocarcinoma patients.

Authors:  Elena Martínez-Terroba; Carmen Behrens; Fernando J de Miguel; Jackeline Agorreta; Eduard Monsó; Laura Millares; Cristina Sainz; Miguel Mesa-Guzman; José Luis Pérez-Gracia; María Dolores Lozano; Javier J Zulueta; Ruben Pio; Ignacio I Wistuba; Luis M Montuenga; María J Pajares
Journal:  J Pathol       Date:  2018-06-20       Impact factor: 7.996

8.  Radiation Therapy Outcomes Models in the Era of Radiomics and Radiogenomics: Uncertainties and Validation.

Authors:  Issam El Naqa; Gaurav Pandey; Hugo Aerts; Jen-Tzung Chien; Christian Nicolaj Andreassen; Andrzej Niemierko; Randall K Ten Haken
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-10-18       Impact factor: 7.038

9.  Machine learning and modeling: Data, validation, communication challenges.

Authors:  Issam El Naqa; Dan Ruan; Gilmer Valdes; Andre Dekker; Todd McNutt; Yaorong Ge; Q Jackie Wu; Jung Hun Oh; Maria Thor; Wade Smith; Arvind Rao; Clifton Fuller; Ying Xiao; Frank Manion; Matthew Schipper; Charles Mayo; Jean M Moran; Randall Ten Haken
Journal:  Med Phys       Date:  2018-08-24       Impact factor: 4.071

10.  External validation of the DHAKA score and comparison with the current IMCI algorithm for the assessment of dehydration in children with diarrhoea: a prospective cohort study.

Authors:  Adam C Levine; Justin Glavis-Bloom; Payal Modi; Sabiha Nasrin; Bita Atika; Soham Rege; Sarah Robertson; Christopher H Schmid; Nur H Alam
Journal:  Lancet Glob Health       Date:  2016-08-23       Impact factor: 26.763

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