Arguments for the biological and predictive relevance of the proportional recovery rule
- Columbia University, United States
- Burke Neurological Institute, United States
- Cereneo, Switzerland
- University Hospital of Zurich, Switzerland
- University of Auckland, New Zealand
- Reade, Netherlands
- Johns Hopkins University, United States
Abstract
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 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.
Data availability
In this work, we reanalyze previously reported datasets. Two of these (referred to as "Winters" and "Zarahn") are included with the submission as a data file. A third (referred to as "Stinear & Byblow") contain data that were collected under ethical approvals that do not permit placing data online. Data can be made available under reasonable request to the author. Researchers interested specifically in the data referred to as "Stinear & Byblow" should contact Cathy Stinear <c.stinear@auckland.ac.nz> and / or Winston Byblow <w.byblow@auckland.ac.nz>, who serve as custodians of the data on behalf of study participants. Please include a description of the planned analyses and a justification for the use of these data specifically. Commercial research using these data is not permitted. All code used in the analyses of all datasets is provided as part of the submission, and all data that can be made publicly available (i.e. "Winters" and "Zarahn") are included with the submission.
Article and author information
Author details
Funding
National Institute of Neurological Disorders and Stroke (R01 NS097423)
- Jeff Goldsmith
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Taraz Lee, University of Michigan, United States
Ethics
Human subjects: Our work reanalyzes several previously reported datasets. These were collected under protocols that ensured informed consent and consent to publish was obtained; details of the protocols and approvals are available in papers originally reporting these datasets.
Version history
- Preprint posted: May 21, 2021 (view preprint)
- Received: May 20, 2022
- Accepted: October 17, 2022
- Accepted Manuscript published: October 18, 2022 (version 1)
- Version of Record published: November 10, 2022 (version 2)
Copyright
© 2022, Goldsmith et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Arguments for the biological and predictive relevance of the proportional recovery rule
eLife 11:e80458.
https://doi.org/10.7554/eLife.80458
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