Can we improve the statistical analysis of stroke trials? Statistical reanalysis of functional outcomes in stroke trials
Background and Purpose—Most large acute stroke trials have been neutral. Functional outcome is usually analyzed using a yes or no answer, eg, death or dependency versus independence. We assessed which statistical approaches are most efficient in analyzing outcomes from stroke trials. Methods—Indivi...
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| Format: | Article |
| Published: |
American Heart Association Inc
2007
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| Online Access: | https://eprints.nottingham.ac.uk/1059/ |
| _version_ | 1848790532841537536 |
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| author | Optimising Analysis of Stroke Trials Collaboration, OAST |
| author_facet | Optimising Analysis of Stroke Trials Collaboration, OAST |
| author_sort | Optimising Analysis of Stroke Trials Collaboration, OAST |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Background and Purpose—Most large acute stroke trials have been neutral. Functional outcome is usually analyzed using a yes or no answer, eg, death or dependency versus independence. We assessed which statistical approaches are most efficient in analyzing outcomes from stroke trials.
Methods—Individual patient data from acute, rehabilitation and stroke unit trials studying the effects of interventions
which alter functional outcome were assessed. Outcomes included modified Rankin Scale, Barthel Index, and “3
questions”. Data were analyzed using a variety of approaches which compare 2 treatment groups. The results for each statistical test for each trial were then compared.
Results—Data from 55 datasets were obtained (47 trials, 54 173 patients). The test results differed substantially so that approaches which use the ordered nature of functional outcome data (ordinal logistic regression, t test, robust ranks test, bootstrapping the difference in mean rank) were more efficient statistically than those which collapse the data into 2 groups (2; ANOVA, P0.001). The findings were consistent across different types and sizes of trial and for the different measures of functional outcome.
Conclusions—When analyzing functional outcome from stroke trials, statistical tests which use the original ordered data are more efficient and more likely to yield reliable results. Suitable approaches included ordinal logistic regression, test, and robust ranks test. |
| first_indexed | 2025-11-14T18:14:07Z |
| format | Article |
| id | nottingham-1059 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:14:07Z |
| publishDate | 2007 |
| publisher | American Heart Association Inc |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-10592020-05-04T20:29:04Z https://eprints.nottingham.ac.uk/1059/ Can we improve the statistical analysis of stroke trials? Statistical reanalysis of functional outcomes in stroke trials Optimising Analysis of Stroke Trials Collaboration, OAST Background and Purpose—Most large acute stroke trials have been neutral. Functional outcome is usually analyzed using a yes or no answer, eg, death or dependency versus independence. We assessed which statistical approaches are most efficient in analyzing outcomes from stroke trials. Methods—Individual patient data from acute, rehabilitation and stroke unit trials studying the effects of interventions which alter functional outcome were assessed. Outcomes included modified Rankin Scale, Barthel Index, and “3 questions”. Data were analyzed using a variety of approaches which compare 2 treatment groups. The results for each statistical test for each trial were then compared. Results—Data from 55 datasets were obtained (47 trials, 54 173 patients). The test results differed substantially so that approaches which use the ordered nature of functional outcome data (ordinal logistic regression, t test, robust ranks test, bootstrapping the difference in mean rank) were more efficient statistically than those which collapse the data into 2 groups (2; ANOVA, P0.001). The findings were consistent across different types and sizes of trial and for the different measures of functional outcome. Conclusions—When analyzing functional outcome from stroke trials, statistical tests which use the original ordered data are more efficient and more likely to yield reliable results. Suitable approaches included ordinal logistic regression, test, and robust ranks test. American Heart Association Inc 2007 Article PeerReviewed Optimising Analysis of Stroke Trials Collaboration, OAST (2007) Can we improve the statistical analysis of stroke trials? Statistical reanalysis of functional outcomes in stroke trials. Stroke, 38 . pp. 1911-1915. ISSN 0039-2499 http://stroke.ahajournals.org/cgi/reprint/38/6/1911 10.1161/STROKEAHA.106.474080 10.1161/STROKEAHA.106.474080 10.1161/STROKEAHA.106.474080 |
| spellingShingle | Optimising Analysis of Stroke Trials Collaboration, OAST Can we improve the statistical analysis of stroke trials? Statistical reanalysis of functional outcomes in stroke trials |
| title | Can we improve the statistical analysis of stroke trials?
Statistical reanalysis of functional outcomes in stroke trials |
| title_full | Can we improve the statistical analysis of stroke trials?
Statistical reanalysis of functional outcomes in stroke trials |
| title_fullStr | Can we improve the statistical analysis of stroke trials?
Statistical reanalysis of functional outcomes in stroke trials |
| title_full_unstemmed | Can we improve the statistical analysis of stroke trials?
Statistical reanalysis of functional outcomes in stroke trials |
| title_short | Can we improve the statistical analysis of stroke trials?
Statistical reanalysis of functional outcomes in stroke trials |
| title_sort | can we improve the statistical analysis of stroke trials?
statistical reanalysis of functional outcomes in stroke trials |
| url | https://eprints.nottingham.ac.uk/1059/ https://eprints.nottingham.ac.uk/1059/ https://eprints.nottingham.ac.uk/1059/ |