Daniel E. Leisman
Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals
Leisman, Daniel E.; Harhay, Michael O.; Lederer, David J.; Abramson, Michael; Adjei, Alex A.; Bakker, Jan; Ballas, Zuhair K.; Barreiro, Esther; Bell, Scott C.; Bellomo, Rinaldo; Bernstein, Jonathan A.; Branson, Richard D.; Brusasco, Vito; Chalmers, James D.; Chokroverty, Sudhansu; Citerio, Giuseppe; Collop, Nancy A.; Cooke, Colin R.; Crapo, James D.; Donaldson, Gavin; Fitzgerald, Dominic A.; Grainger, Emma; Hale, Lauren; Herth, Felix J.; Kochanek, Patrick M.; Marks, Guy; Moorman, J. Randall; Ost, David E.; Schatz, Michael; Sheikh, Aziz; Smyth, Alan R.; Stewart, Iain; Stewart, Paul W.; Swenson, Erik R.; Szymusiak, Ronald; Teboul, Jean-Louis; Vincent, Jean-Louis; Wedzicha, Jadwiga A.; Maslove, David M.
Authors
Michael O. Harhay
David J. Lederer
Michael Abramson
Alex A. Adjei
Jan Bakker
Zuhair K. Ballas
Esther Barreiro
Scott C. Bell
Rinaldo Bellomo
Jonathan A. Bernstein
Richard D. Branson
Vito Brusasco
James D. Chalmers
Sudhansu Chokroverty
Giuseppe Citerio
Nancy A. Collop
Colin R. Cooke
James D. Crapo
Gavin Donaldson
Dominic A. Fitzgerald
Emma Grainger
Lauren Hale
Felix J. Herth
Patrick M. Kochanek
Guy Marks
J. Randall Moorman
David E. Ost
Michael Schatz
Aziz Sheikh
Alan R. Smyth
Iain Stewart
Paul W. Stewart
Erik R. Swenson
Ronald Szymusiak
Jean-Louis Teboul
Jean-Louis Vincent
Jadwiga A. Wedzicha
David M. Maslove
Abstract
Prediction models aim to use available data to predict a health state or outcome that has not yet been observed. Prediction is primarily relevant to clinical practice, but is also used in research, and administration. While prediction modeling involves estimating the relationship between patient factors and outcomes, it is distinct from casual inference. Prediction modeling thus requires unique considerations for development, validation, and updating. This document represents an effort from editors at 31 respiratory, sleep, and critical care medicine journals to consolidate contemporary best practices and recommendations related to prediction study design, conduct, and reporting. Herein, we address issues commonly encountered in submissions to our various journals. Key topics include considerations for selecting predictor variables, operationalizing variables, dealing with missing data, the importance of appropriate validation, model performance measures and their interpretation, and good reporting practices. Supplemental discussion covers emerging topics such as model fairness, competing risks, pitfalls of “modifiable risk factors”, measurement error, and risk for bias. This guidance is not meant to be overly prescriptive; we acknowledge that every study is different, and no set of rules will fit all cases. Additional best practices can be found in the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, to which we refer readers for further details.
Citation
Leisman, D. E., Harhay, M. O., Lederer, D. J., Abramson, M., Adjei, A. A., Bakker, J., …Maslove, D. M. (2020). Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals. Critical Care Medicine, 48(5), 623-633. https://doi.org/10.1097/ccm.0000000000004246
Journal Article Type | Article |
---|---|
Acceptance Date | Mar 11, 2020 |
Publication Date | 2020-05 |
Deposit Date | Apr 20, 2020 |
Publicly Available Date | Apr 20, 2020 |
Journal | Critical Care Medicine |
Print ISSN | 0090-3493 |
Publisher | Lippincott, Williams & Wilkins |
Peer Reviewed | Peer Reviewed |
Volume | 48 |
Issue | 5 |
Pages | 623-633 |
DOI | https://doi.org/10.1097/ccm.0000000000004246 |
Keywords | Critical Care and Intensive Care Medicine |
Public URL | https://nottingham-repository.worktribe.com/output/4319007 |
Publisher URL | https://journals.lww.com/ccmjournal/Fulltext/2020/05000/Development_and_Reporting_of_Prediction_Models_.3.aspx |
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