Self-Controlled Case Series

The design compares exposed and unexposed time within each person who had the event, so the relevant contrast is internal to cases. By conditioning on the number of events each case experienced, the individual's baseline rate cancels out of the likelihood, and with it every fixed characteristic. Non-cases contribute no information about within-person timing, so they are simply not needed. Farrington's original derivation makes this explicit: the conditional likelihood depends only on the exposure status and length of each person's intervals, which is why the method is so efficient with case-only data.

It eliminates all confounders that are constant within a person over the observation period — sex, genetics, chronic comorbidity, stable socioeconomic factors — because these are absorbed into the conditioned-out baseline. It does not automatically handle time-varying confounders. The most important of these is age, which is modeled explicitly with age categories or splines; seasonality and other time trends must likewise be included if they move with exposure timing. Whitaker and colleagues stress that residual time-varying confounding is the main threat once fixed confounders are removed.

Three assumptions matter most. First, events should be rare within the observation window so that having an event does not deplete later time at risk. Second, the event should not change the probability of subsequent exposure — for instance, if a reaction leads clinicians to withhold further doses, this event-dependent exposure biases the standard estimate. Third, the event should not end observation, as a fatal outcome does. Each violation has a tailored extension — pre-exposure windows, modified likelihoods for event-dependent exposure, and methods for event-dependent observation — described in the Whitaker tutorial, but the basic model should not be used when they hold without correction.