TY - JOUR
T1 - Arguments for the biological and predictive relevance of the proportional recovery rule
AU - Goldsmith, Jeff
AU - Kitago, Tomoko
AU - de la Garza, Angel Garcia
AU - Kundert, Robinson
AU - Luft, Andreas
AU - Stinear, Cathy
AU - Byblow, Winston D.
AU - Kwakkel, Gert
AU - Krakauer, John W.
N1 - Funding Information: We acknowledge the EXPLICIT-stroke consortium for collecting data. This data collection was supported by funding from the Royal Dutch Society of Physical Therapy and the Netherlands Organization for Health Research and Development (ZonMw; Grant No. 89000001). JG’s work was supported in part by NIH-funded R01NS097423. Publisher Copyright: © 2022, eLife Sciences Publications Ltd. All rights reserved.
PY - 2022
Y1 - 2022
N2 - 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 explic-itly 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.
AB - 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 explic-itly 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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85141537679&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/36255057
U2 - https://doi.org/10.7554/eLife.80458
DO - https://doi.org/10.7554/eLife.80458
M3 - Article
C2 - 36255057
SN - 2050-084X
VL - 11
JO - eLife
JF - eLife
M1 - e80458
ER -