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Arguments for the biological and predictive relevance of the proportional recovery rule

The proportional recovery rule (PRR) posits that most stroke survivors can expect to reduce a fixed proportion of their motor impairment. As a statistical model, the PRR explicitly relates change scores to baseline values – an approach that arises in many scientific domains but has the potential to...

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Autores principales: Goldsmith, Jeff, Kitago, Tomoko, Garcia de la Garza, Angel, Kundert, Robinson, Luft, Andreas, Stinear, Cathy, Byblow, Winston D, Kwakkel, Gert, Krakauer, John W
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9648971/
https://www.ncbi.nlm.nih.gov/pubmed/36255057
http://dx.doi.org/10.7554/eLife.80458
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author Goldsmith, Jeff
Kitago, Tomoko
Garcia de la Garza, Angel
Kundert, Robinson
Luft, Andreas
Stinear, Cathy
Byblow, Winston D
Kwakkel, Gert
Krakauer, John W
author_facet Goldsmith, Jeff
Kitago, Tomoko
Garcia de la Garza, Angel
Kundert, Robinson
Luft, Andreas
Stinear, Cathy
Byblow, Winston D
Kwakkel, Gert
Krakauer, John W
author_sort Goldsmith, Jeff
collection PubMed
description The proportional recovery rule (PRR) posits that most stroke survivors can expect to reduce a fixed proportion of their motor impairment. As a statistical model, the PRR explicitly relates change scores to baseline values – an approach that arises in many scientific domains but has the potential to introduce artifacts and flawed conclusions. We describe approaches that can assess associations between baseline and changes from baseline while avoiding artifacts due either to mathematical coupling or to regression to the mean. We also describe methods that can compare different biological models of recovery. Across several real datasets in stroke recovery, we find evidence for non-artifactual associations between baseline and change, and support for the PRR compared to alternative models. We also introduce a statistical perspective that can be used to assess future models. We conclude that the PRR remains a biologically relevant model of stroke recovery.
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spelling pubmed-96489712022-11-15 Arguments for the biological and predictive relevance of the proportional recovery rule Goldsmith, Jeff Kitago, Tomoko Garcia de la Garza, Angel Kundert, Robinson Luft, Andreas Stinear, Cathy Byblow, Winston D Kwakkel, Gert Krakauer, John W eLife Neuroscience The proportional recovery rule (PRR) posits that most stroke survivors can expect to reduce a fixed proportion of their motor impairment. As a statistical model, the PRR explicitly relates change scores to baseline values – an approach that arises in many scientific domains but has the potential to introduce artifacts and flawed conclusions. We describe approaches that can assess associations between baseline and changes from baseline while avoiding artifacts due either to mathematical coupling or to regression to the mean. We also describe methods that can compare different biological models of recovery. Across several real datasets in stroke recovery, we find evidence for non-artifactual associations between baseline and change, and support for the PRR compared to alternative models. We also introduce a statistical perspective that can be used to assess future models. We conclude that the PRR remains a biologically relevant model of stroke recovery. eLife Sciences Publications, Ltd 2022-10-18 /pmc/articles/PMC9648971/ /pubmed/36255057 http://dx.doi.org/10.7554/eLife.80458 Text en © 2022, Goldsmith et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Goldsmith, Jeff
Kitago, Tomoko
Garcia de la Garza, Angel
Kundert, Robinson
Luft, Andreas
Stinear, Cathy
Byblow, Winston D
Kwakkel, Gert
Krakauer, John W
Arguments for the biological and predictive relevance of the proportional recovery rule
title Arguments for the biological and predictive relevance of the proportional recovery rule
title_full Arguments for the biological and predictive relevance of the proportional recovery rule
title_fullStr Arguments for the biological and predictive relevance of the proportional recovery rule
title_full_unstemmed Arguments for the biological and predictive relevance of the proportional recovery rule
title_short Arguments for the biological and predictive relevance of the proportional recovery rule
title_sort arguments for the biological and predictive relevance of the proportional recovery rule
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9648971/
https://www.ncbi.nlm.nih.gov/pubmed/36255057
http://dx.doi.org/10.7554/eLife.80458
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