The SCCS design

The SCCS design, in contrast to standard epidemiological observational designs like the cohort and case– control design, offers a more time- and cost-efficient approach. This efficiency is due to the larger sample sizes required by the standard designs. Further, the SCCS method automatically adjusts for known and unknown fixed confounders. The latter can be a significant challenge in standard designs. The SCCS method splits an observation period into one or more risk periods and one or more control periods. The risk periods are relative to an exposure event, whereas the observation period is either fixed or relative to the exposure event. Often, one adds time or age adjustments during the observation period. The basic idea is to compare incidence rates for the risk periods with the control period while adjusting for time or age and cases. The SCCS design originates from the desire to estimate the relative effect of vaccines, such as the MMR, on adverse events like meningitis. Compared with the classical design, it is a matter of asking when instead of who. I will discuss the SCCS design and present the Stata command sccsdta, which transforms datasets of times for events and exposures by cases into datasets marked into risk and control periods as well as time or age periods. After the dataset transformation, the analysis is simple, using fixed-effect Poisson regression.