Effect Size Estimates for the ESCAPE trial: Proportional Odds Regression versus other statistical methods - Sajobi, Tolulope T. (Stroke, 2015)
BACKGROUND AND PURPOSE: Ordinal outcomes, such as modified Rankin scale (mRS), are the standard primary endpoints in acute stroke trials. Regression models for assessing treatment efficacy after adjusting for baseline covariates have been developed for continuous, binary, or ordinal endpoints. 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 datasets from IMS III and PROACT 2 trials. Patients with baseline non-contrast CT ASPECTS score > 5, baseline CT angiography (CTA) or conventional angiogram showing an intracranial internal carotid artery (ICA) or middle cerebral artery trunk (M-1) occlusion, adequate collateral circulation shown on CTA, and treatment times of non-contrast CT to groin puncture of 90 minutes or less, 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.