Strengthening Acute Stroke Trials Through Optimal Use of Disability End Points

Author:

Young Fiona B.1,Lees Kennedy R.1,Weir Christopher J.1

Affiliation:

1. From the Division of Cardiovascular and Medical Sciences, University of Glasgow, Gardiner Institute, Western Infirmary (F.B.Y., K.R.L.), and Robertson Centre for Biostatistics, University of Glasgow (C.J.W.), Glasgow, Scotland.

Abstract

Background and Purpose— Suboptimal choices of primary end point for acute stroke trials may have contributed to inconclusive results. The Barthel Index (BI) and Rankin Scale (RS) have been widely used and analyzed in various ways. We sought to investigate the most powerful end point for use in acute stroke trials. Methods— Data from the Glycine Antagonist in Neuroprotection (GAIN) International Trial were used to simulate 24 000 clinical trials exploring various patterns and magnitudes of treatment effect and thus to estimate the statistical power for a range of end points based on the BI or RS. Results— RS end points were more powerful than BI end points. End points dichotomized toward the favorable extreme of either scale or adjusted according to baseline prognosis (“patient-specific” end point) were among the most powerful. Combining RS and BI in a “global” end point was also successful. Improvements in statistical power indicated that using a RS end point instead of BI ≥60 could reduce the sample size by up to 84% (95% CI, 80% to 87%), 73% (95% CI, 68% to 79%) for a patient-specific BI end point, or 81% (95% CI, 76% to 85%) for a global end point. Conclusions— The RS and global end points are preferable to BI end points; the position of the cut point is also important. Better choices of end point substantially strengthen trial power for a given trial size or allow reduced sample sizes without loss of statistical power.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Advanced and Specialised Nursing,Cardiology and Cardiovascular Medicine,Clinical Neurology

Reference27 articles.

1. Bland M. An Introduction to Medical Statistics. 3rd ed. Oxford UK: Oxford University Press; 2000.

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