A causal framework for the self-controlled case series design

Abstract Vaccine safety surveillance programs that monitor possible short-term rare adverse events following vaccination usually only have access to data on vaccinated individuals who experienced the event of interest. The Self-Controlled Case Series design employs such data and compares the risk of the event in an “risky” period immediately after vaccination to that in a “baseline risk” period where the transient risk should be gone. To ensure valid analysis, some assumptions have been given in the literature while others are made implicitly through parametric modeling. In this work, we provide a complete formal causal framework for the Self-Controlled Cases Series design. We provide sufficient conditions for a causal interpretation of the contrasts estimated from the data on exposed individuals who experienced the event. These conditions are intuitive but often cannot be tested from the available data. We describe practical settings where these conditions may be violated. When the conditions are not met, the contrasts estimated in practice do not clearly relate to any quantities of causal interest, and should therefore be interpreted with caution.