Published Online
on July 8, 2004

A more recent version of this article appeared on September 1, 2004

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Submitted on May 14, 2004
Revised on June 2, 2004
Accepted on June 3, 2004

Targeting Neuroprotection Clinical Trials to Ischemic Stroke Patients With Potential to Benefit From Therapy

Christopher J. Weir PhD^*; Markku Kaste MD; Kennedy R. Lees MD, FRCP; for the Glycine Antagonist in Neuroprotection (GAIN) International Steering Committee and Investigators

From the Division of Cardiovascular and Medical Sciences (C.J.W., K.R.L.), University of Glasgow, Gardiner Institute, Western Infirmary, Glasgow, UK; the Department of Neurology (M.K.), Helsinki University Central Hospital, Helsinki, Finland; and the Robertson Centre for Biostatistics (C.J.W), University of Glasgow, Glasgow, UK.

^* To whom correspondence should be addressed. E-mail: c.j.weir{at}clinmed.gla.ac.uk.

Background and Purpose--Clinical trials of neuroprotective drugs have had limited success. We investigated whether selecting patients according to prognostic features would improve the statistical power of a trial to identify an efficacious treatment.

Methods--Using placebo data from the Glycine Antagonist in Neuroprotection (GAIN) International and National Institute of Neurological Disorders and Stroke (NINDS) recombinant tissue plasminogen activator (rtPA) clinical trials, we developed and validated simple prognostic models for stroke trial end points: Barthel Index 95, modified Rankin Scale 1, National Institutes of Health Stroke Scale 1, and Glasgow Outcome Scale=1. Using these models, we simulated 1000 clinical trials and estimated, under several hypothetical treatment effect patterns of neuroprotection, the effect on statistical power of including only patients with moderate prognosis. We calculated the number of patients that would have to be enrolled to maintain the statistical power achieved in selecting the whole trial population. Reanalysis of actual data from the NINDS rtPA trials confirmed the results independently.

Results--Selecting patients with moderate prognosis (predicted probability of favorable outcome 0.2 to 0.8) enabled a sample size reduction, without loss of statistical power, of between 54.6% (51.3% to 57.6%) and 68.6% (66.0% to 71.1%), depending on the treatment effect pattern and outcome measure. These benefits were largely due to the exclusion of patients with poor prognosis.

Conclusions--Targeting patients with potential to benefit enables a substantial sample size reduction without compromising statistical power or duration of recruitment. As part of a broader trial design strategy, informed use of prognostic data available acutely would help in identifying effective neuroprotective treatments.

Key words: clinical trials • neuroprotection • prognosis

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