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Percentage of progressors in imaging: can we ignore regressors?

Stopping or preventing structural progression is a goal common to all inflammatory rheumatic diseases. Imaging may capture structural progression across diseases, but is susceptible to measurement error. Progression can be analysed as a continuous change score over time (eg, mean change of the van d...

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Autores principales: Sepriano, Alexandre, Ramiro, Sofia, Landewé, Robert, Dougados, Maxime, van der Heijde, Desirée
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6397429/
https://www.ncbi.nlm.nih.gov/pubmed/30886736
http://dx.doi.org/10.1136/rmdopen-2018-000848
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author Sepriano, Alexandre
Ramiro, Sofia
Landewé, Robert
Dougados, Maxime
van der Heijde, Desirée
author_facet Sepriano, Alexandre
Ramiro, Sofia
Landewé, Robert
Dougados, Maxime
van der Heijde, Desirée
author_sort Sepriano, Alexandre
collection PubMed
description Stopping or preventing structural progression is a goal common to all inflammatory rheumatic diseases. Imaging may capture structural progression across diseases, but is susceptible to measurement error. Progression can be analysed as a continuous change score over time (eg, mean change of the van der Heijde-modified Sharp score) or as a binary change score (eg, percentage of progressors according to the modified New York criteria). Here, we argue that the former takes measurement error into account while the latter ignores it, which may lead to spurious conclusions. We will argue that assumptions underlying commonly used binary definitions of progression are false and we propose a method that incorporates (inevitable) measurement error.
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spelling pubmed-63974292019-03-18 Percentage of progressors in imaging: can we ignore regressors? Sepriano, Alexandre Ramiro, Sofia Landewé, Robert Dougados, Maxime van der Heijde, Desirée RMD Open Imaging Stopping or preventing structural progression is a goal common to all inflammatory rheumatic diseases. Imaging may capture structural progression across diseases, but is susceptible to measurement error. Progression can be analysed as a continuous change score over time (eg, mean change of the van der Heijde-modified Sharp score) or as a binary change score (eg, percentage of progressors according to the modified New York criteria). Here, we argue that the former takes measurement error into account while the latter ignores it, which may lead to spurious conclusions. We will argue that assumptions underlying commonly used binary definitions of progression are false and we propose a method that incorporates (inevitable) measurement error. BMJ Publishing Group 2019-02-26 /pmc/articles/PMC6397429/ /pubmed/30886736 http://dx.doi.org/10.1136/rmdopen-2018-000848 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. 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/.
spellingShingle Imaging
Sepriano, Alexandre
Ramiro, Sofia
Landewé, Robert
Dougados, Maxime
van der Heijde, Desirée
Percentage of progressors in imaging: can we ignore regressors?
title Percentage of progressors in imaging: can we ignore regressors?
title_full Percentage of progressors in imaging: can we ignore regressors?
title_fullStr Percentage of progressors in imaging: can we ignore regressors?
title_full_unstemmed Percentage of progressors in imaging: can we ignore regressors?
title_short Percentage of progressors in imaging: can we ignore regressors?
title_sort percentage of progressors in imaging: can we ignore regressors?
topic Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6397429/
https://www.ncbi.nlm.nih.gov/pubmed/30886736
http://dx.doi.org/10.1136/rmdopen-2018-000848
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