The European Medicines Agency (EMA) and the European Medicines Regulatory Network established a coordination centre to provide timely and reliable evidence on the use, safety and effectiveness of medicines for human use, including vaccines, from real world healthcare databases across the European Union (EU). This capability is called the Data Analysis and Real World Interrogation Network (DARWIN EU®).

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Study Type/s

Self-controlled case series are classified as ‘complex’ (C3) analyses.

Study Design

Self-controlled case series (SCCS).


SCCS will include one or more cohort/s of people who suffer a specified safety event/group of events at least once in their record/s, hence their denomination as “case series”. Additional eligibility criteria could apply based on socio-demographics or clinical characteristics.


We will pre-define follow-up periods according to exposure history. Typically, participants in an SCCS will be followed for some time before (pre-exposure), during (exposed) and post-exposure to a medicinal product. Sometimes, a washout is imposed before the beginning of the exposure period. Pre- and post-exposure periods will be considered as “baseline” or “unexposed”, whilst treatment episode/s are “exposed”. The washout period will be disregarded and not accounted for in the analyses. See Figure 3 for an illustration.

In some analyses, only the first event will be considered for each participant to minimise biases, with follow-up censored after that first event.


One or more study outcomes will be pre-specified, based on previous DARWIN EU algorithms or newly developed and validated ones. Ideally, outcomes should be acute in presentation and with a clear and accurate diagnosis date.

In addition, a long list of negative control outcomes will be assessed, which are known to have no causal association with the drug/s or medicinal product/s under study.


Details will be discussed during programming of pipelines, but SCCS will include:

  • Large-scale characterisation of SCCS participants at the time of diagnosis (index date), including all recorded features available in the data before or at that date, based on SNOMED code/s
  • Pre-specified patient-level characteristics on before or at index date, based on pre-existing cohorts or definitions (e.g., history of type 2 diabetes, or Charlson comorbidity index).
  • Pre-specified patient-level characteristics on before or at index date, based on concepts and descendants where no previously validated algorithms are available
  • Incidence rates during exposed and unexposed time
  • Diagnostic/s:
    • Event-exposure independence: a histogram of the time between the event date and the end of observation for individuals censored and uncensored will be plotted to assess potential for event-dependent observation time
    • Analyses will not be conducted where there is insufficient data, based on a pre-specified minimum detectable rate ratio (e.g., MDRR>5)
    • Optional: In addition to the two above, residual confounding/systematic error will be available for estimation, as based on the distribution of results from the negative control outcome analyses
  • Incidence rate ratios and 95% confidence intervals will be estimated using conditional Poisson regression models, comparing the exposed vs the baseline period.
  • Adjusted incidence rate ratios and 95% confidence intervals will be calculated after adjustment for age and seasonality
  • Optionally, calibrated incidence rate ratios will be estimated after empirical calibration of the adjusted incidence rate ratio based on the observed systematic error