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Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine

INTRODUCTION: While there are guidelines for reporting on observational studies (eg, Strengthening the Reporting of Observational Studies in Epidemiology, Reporting of Studies Conducted Using Observational Routinely Collected Health Data Statement), estimation of causal effects from both observation...

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Autores principales: Xu, Jie, Guo, Yi, Wang, Fei, Xu, Hua, Lucero, Robert, Bian, Jiang, Prosperi, Mattia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214357/
https://www.ncbi.nlm.nih.gov/pubmed/35725267
http://dx.doi.org/10.1136/bmjopen-2021-059715
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author Xu, Jie
Guo, Yi
Wang, Fei
Xu, Hua
Lucero, Robert
Bian, Jiang
Prosperi, Mattia
author_facet Xu, Jie
Guo, Yi
Wang, Fei
Xu, Hua
Lucero, Robert
Bian, Jiang
Prosperi, Mattia
author_sort Xu, Jie
collection PubMed
description INTRODUCTION: While there are guidelines for reporting on observational studies (eg, Strengthening the Reporting of Observational Studies in Epidemiology, Reporting of Studies Conducted Using Observational Routinely Collected Health Data Statement), estimation of causal effects from both observational data and randomised experiments (eg, A Guideline for Reporting Mediation Analyses of Randomised Trials and Observational Studies, Consolidated Standards of Reporting Trials, PATH) and on prediction modelling (eg, Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis), none is purposely made for deriving and validating models from observational data to predict counterfactuals for individuals on one or more possible interventions, on the basis of given (or inferred) causal structures. This paper describes methods and processes that will be used to develop a Reporting Guideline for Causal and Counterfactual Prediction Models (PRECOG). METHODS AND ANALYSIS: PRECOG will be developed following published guidance from the Enhancing the Quality and Transparency of Health Research (EQUATOR) network and will comprise five stages. Stage 1 will be meetings of a working group every other week with rotating external advisors (active until stage 5). Stage 2 will comprise a systematic review of literature on counterfactual prediction modelling for biomedical sciences (registered in Prospective Register of Systematic Reviews). In stage 3, a computer-based, real-time Delphi survey will be performed to consolidate the PRECOG checklist, involving experts in causal inference, epidemiology, statistics, machine learning, informatics and protocols/standards. Stage 4 will involve the write-up of the PRECOG guideline based on the results from the prior stages. Stage 5 will seek the peer-reviewed publication of the guideline, the scoping/systematic review and dissemination. ETHICS AND DISSEMINATION: The study will follow the principles of the Declaration of Helsinki. The study has been registered in EQUATOR and approved by the University of Florida’s Institutional Review Board (#202200495). Informed consent will be obtained from the working groups and the Delphi survey participants. The dissemination of PRECOG and its products will be done through journal publications, conferences, websites and social media.
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spelling pubmed-92143572022-07-07 Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine Xu, Jie Guo, Yi Wang, Fei Xu, Hua Lucero, Robert Bian, Jiang Prosperi, Mattia BMJ Open Health Informatics INTRODUCTION: While there are guidelines for reporting on observational studies (eg, Strengthening the Reporting of Observational Studies in Epidemiology, Reporting of Studies Conducted Using Observational Routinely Collected Health Data Statement), estimation of causal effects from both observational data and randomised experiments (eg, A Guideline for Reporting Mediation Analyses of Randomised Trials and Observational Studies, Consolidated Standards of Reporting Trials, PATH) and on prediction modelling (eg, Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis), none is purposely made for deriving and validating models from observational data to predict counterfactuals for individuals on one or more possible interventions, on the basis of given (or inferred) causal structures. This paper describes methods and processes that will be used to develop a Reporting Guideline for Causal and Counterfactual Prediction Models (PRECOG). METHODS AND ANALYSIS: PRECOG will be developed following published guidance from the Enhancing the Quality and Transparency of Health Research (EQUATOR) network and will comprise five stages. Stage 1 will be meetings of a working group every other week with rotating external advisors (active until stage 5). Stage 2 will comprise a systematic review of literature on counterfactual prediction modelling for biomedical sciences (registered in Prospective Register of Systematic Reviews). In stage 3, a computer-based, real-time Delphi survey will be performed to consolidate the PRECOG checklist, involving experts in causal inference, epidemiology, statistics, machine learning, informatics and protocols/standards. Stage 4 will involve the write-up of the PRECOG guideline based on the results from the prior stages. Stage 5 will seek the peer-reviewed publication of the guideline, the scoping/systematic review and dissemination. ETHICS AND DISSEMINATION: The study will follow the principles of the Declaration of Helsinki. The study has been registered in EQUATOR and approved by the University of Florida’s Institutional Review Board (#202200495). Informed consent will be obtained from the working groups and the Delphi survey participants. The dissemination of PRECOG and its products will be done through journal publications, conferences, websites and social media. BMJ Publishing Group 2022-06-17 /pmc/articles/PMC9214357/ /pubmed/35725267 http://dx.doi.org/10.1136/bmjopen-2021-059715 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Health Informatics
Xu, Jie
Guo, Yi
Wang, Fei
Xu, Hua
Lucero, Robert
Bian, Jiang
Prosperi, Mattia
Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine
title Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine
title_full Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine
title_fullStr Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine
title_full_unstemmed Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine
title_short Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine
title_sort protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine
topic Health Informatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214357/
https://www.ncbi.nlm.nih.gov/pubmed/35725267
http://dx.doi.org/10.1136/bmjopen-2021-059715
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