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Transl Stroke Res. 2016 Oct;7(5):388-94. doi: 10.1007/s12975-016-0488-0. Epub 2016 Aug 8.

How to Measure Recovery? Revisiting Concepts and Methods for Stroke Studies.

Author information

1
University Grenoble Alpes, AGEIS EA 7407, Grenoble, France. marc.hommel@ujf-grenoble.fr.
2
CHU Grenoble, Pôle Recherche, Grenoble, France. marc.hommel@ujf-grenoble.fr.
3
CHU Grenoble, Pôle Psychiatrie Neurologie, Stroke unit, Grenoble, France.
4
INSERM U919, Stroke unit, CHU, University of Caen Basse Normandie, Caen, France.
5
University Grenoble Alpes, AGEIS EA 7407, Grenoble, France.
6
CHU Grenoble, Pôle Recherche, Grenoble, France.

Abstract

In clinical trials, assessing efficacy is based on validated scales, and the primary endpoint is usually based on a single scale. The aim of the review is to revisit the concepts and methods to design and analyze studies focused on restoration, recovery and or compensation. These studies are becoming more frequent with the development of restorative medicine. After discussing the definitions of recovery, we address the concept of recovery as the regain of lost capabilities, when the patient reaches a new equilibrium. Recovery is a dynamic process which assessment includes information from initial and final status, their difference, the difference between the final status of the patient and normality, and the speed of restoration. Finally, recovery can be assessed either for a specific function (focal restoration) or for a more global restoration. A single scale is not able to assess all the facets of a skill or a function, therefore complementary information should be collected and analyzed simultaneously to be tested in a single analysis. We are suggesting that recovery should be considered as a latent variable and therefore cannot be measured in pure form. We are also suggesting to customize the data collection and analysis according to the characteristics of the subjects, the mechanisms of action and consequences of the intervention. Moreover, recovery trials should benefit from latent variable analysis methods. Structural equation modeling is likely the best candidate for this approach applicable in pre-clinical and clinical studies.

KEYWORDS:

Clinical scale; Intervention evaluation; Latent variable; Methodology; Modeling; Study design

PMID:
27498680
DOI:
10.1007/s12975-016-0488-0
[Indexed for MEDLINE]

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