Randomized Controlled Trials (RCTs) are considered the gold standard for measuring the efficacy of medical interventions. However, RCTs are expensive, and use a limited population. Techniques to estimate the effects of stroke interventions from observational data that minimize confounding would be useful. We used regression discontinuity design (RDD), a technique well-established in economics, on the Get With The Guidelines-Stroke (GWTG-Stroke) data set. RDD, based on regression, measures the occurrence of a discontinuity in an outcome (e.g., home discharge) as a function of an intervention (e.g., alteplase) that becomes significantly more likely when crossing the threshold of a continuous variable (e.g., time from symptom onset). The technique assumes that patients near either side of a threshold (e.g., 2.99 and 3.01 hours from symptom onset) are indistinguishable other than the use of the treatment. We compared outcomes of patients whose estimated onset to treatment time fell on either side of the treatment threshold for three cohorts of patients in the GWTG-Stroke data set that spanned different treatment thresholds for alteplase (3 hours, 2003-2007, N=1869; 3 hours, 2009-2016, N=13,086, and 4.5 hours, 2009-2016, N=6,550). Patient demographic characteristics were overall similar across the treatment thresholds. We did not find evidence of a discontinuity in discharge disposition at any treatment threshold attributable to alteplase. Potential reasons for failing to find an effect include violation of some RDD assumptions in clinical care and large sample sizes required.