Abstract
BACKGROUND AND PURPOSE: Ordinal outcomes, such as modified Rankin Scale (mRS), are the standard primary end points in acute stroke trials. Regression models for assessing treatment efficacy after adjusting for baseline covariates have been developed for continuous, binary, or ordinal end points. There has been no consensus on the best choice of method for analyzing these data. METHODS: We compared several regression models for assessing treatment efficacy in acute stroke trials using existing data sets from the Interventional Management of Stroke-III and Prolyse in Acute Cerebral Thromboembolism II (PROACT-2) trials. Patients with baseline non-contrast computed tomographic Alberta Stroke Program Early CT Score (ASPECTS) > 5, baseline computed tomographic angiography, or conventional angiogram showing an intracranial internal carotid artery or middle cerebral artery trunk (M-1) occlusion, adequate collateral circulation shown on computed tomographic angiography, and treatment times of non-contrast computed tomographic to groin puncture of ≤90 minutes, were included. Monte Carlo techniques were used to compare the statistical power of these regression models under a variety of simulated data analytic scenarios. RESULTS: Binary logistic regression showed greater power when the treatment is predicted to show evidence of benefit on one end of the mRS with no other gains across other levels of the scale. Proportional odds regression showed greater power when the treatment is predicted to show evidence of improvement on both ends of the mRS. CONCLUSIONS: The mRS distribution for both treatment and control groups influences the power of the investigated statistical models to assess treatment efficacy. A careful evaluation of the expected outcome distribution across the mRS scale is required to determine the best choice of primary analysis.