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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2022
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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. |
format | Online Article Text |
id | pubmed-9648971 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
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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