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(Stroke. 2004;35:e43.)
© 2004 American Heart Association, Inc.
Research Reports |
From the Departments of Neurology (K.C.J., E.C.H.) and Health Evaluation Sciences (K.C.H., D.P.W.), University of Virginia, Charlottesville, and Case Western Reserve University and MetroHealth Medical Center, Department of Medicine, Cleveland, Ohio (A.F.C.).
Correspondence to Karen C. Johnston, MD, MSc, University of Virginia Health System, Department of Neurology, Box 800394, Charlottesville, VA 22908. E-mail kj4v{at}virginia.edu
Background and Purpose The ischemic stroke population is heterogeneous. Even in balanced randomized trials, patient heterogeneity biases estimates of the treatment effect toward no effect when dichotomous end points are used. Risk adjustment statistically addresses some of the heterogeneity and can reduce bias in the treatment effect estimate. The purpose of this study was to estimate the treatment effect of tissue plasminogen activator (tPA) in the National Institute of Neurological Disorders and Stroke (NINDS) tPA data set with and without adjustment for baseline differences.
Methods Using a prespecified predictive model, we calculated unadjusted and risk-adjusted odds ratios (ORs) for favorable outcome for the Barthel Index, National Institutes of Health Stroke Scale, and Glasgow Outcome Scale for the patients in the NINDS tPA stroke trial. To assess the importance of the difference, a new sample size was calculated through the use of the risk-adjusted analysis.
Results We analyzed 615 subjects. The ORs for the Barthel Index were 1.76 (unadjusted) and 2.04 (adjusted). The National Institutes of Health Stroke Scale and Glasgow Outcome Scale analyses also demonstrated increased ORs after adjustment. The estimated sample size required for the adjusted comparison was 13% smaller than the unadjusted sample.
Conclusions Risk adjustment in this data set suggests that the true treatment effect was larger than estimated by the unadjusted analysis. Stroke clinical trials should include prospective risk adjustment methodologies.
Key Words: cerebral ischemia models, statistical prognosis risk adjustment stroke outcome
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