Unusual problems in statistical design were faced by Rapid Early Action for Coronary Treatment (REACT), a multisite trial testing a community intervention to reduce the delay between onset of symptoms of acute myocardial infarction (MI) and patients' arrival at a hospital emergency department. In 20 pair-matched U.S. communities, hospital staff members recorded delay time throughout a 4-month baseline period and the subsequent 18-month intervention period, during which one randomly selected community of each pair received a campaign of public and professional education. To exploit the continual nature of its data-collection protocol, REACT estimated the trend of delay time separately in each community by linear regression, adjusting for age, sex, and history of MI, and compared the ten adjusted slopes from intervention communities with those from control communities by a paired t-test. Power calculations based on the analytical model showed that with K = 600-800 cases per community, REACT would have 80% power to demonstrate a differential reduction of 30 min in mean delay time between intervention and control communities, as well as effects on a variety of secondary outcomes. Sensitivity analysis confirmed that the number of communities was optimal within constraints of funding and that the detectable effect depended weakly on the effectiveness of matching but strongly on K, helping the investigators set operational priorities. The methodologic strategy developed for REACT should prove useful in the design of similar trials in the future.