Development and reporting of prediction models: guidance for authors from editors of respiratory, sleep, and critical care journals
Author(s)
Type
Journal Article
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.
Date Issued
2020-05-01
Date Acceptance
2020-01-14
Citation
Critical Care Medicine, 2020, 48 (5), pp.623-633
ISSN
0090-3493
Publisher
Lippincott, Williams & Wilkins
Start Page
623
End Page
633
Journal / Book Title
Critical Care Medicine
Volume
48
Issue
5
Copyright Statement
© 2020 The Author(s). Published by Wolters Kluwer Health,
Inc. on behalf of the Society of Critical Care Medicine and Wolters Kluwer
Health, Inc. This is an open-access article distributed under the terms of
the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share
the work provided it is properly cited. The work cannot be changed in any
way or used commercially without permission from the journal.
Inc. on behalf of the Society of Critical Care Medicine and Wolters Kluwer
Health, Inc. This is an open-access article distributed under the terms of
the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share
the work provided it is properly cited. The work cannot be changed in any
way or used commercially without permission from the journal.
Sponsor
Medical Research Council (MRC)
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000528031100014&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
G1001372
Subjects
Science & Technology
Life Sciences & Biomedicine
Critical Care Medicine
General & Internal Medicine
critical care
pulmonary medicine
prediction models
sleep medicine
INDIVIDUAL PROGNOSIS
RISK
REGRESSION
EXPLANATION
VALIDATION
Publication Status
Published
Date Publish Online
2020-05