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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New user active comparator cohort

Study Type/s

New user active comparator cohort studies are classified as ‘complex’ analyses.

Study Design

New user cohorts.

Participant/s

At least two cohorts, including a cohort of new users of at least one drug/medicinal product under investigation (target cohort) and a cohort of new users of at least one drug/medicinal product as an active comparator (comparator cohort). Typically, new user cohorts exclude previous users of either cohort in the previous year as well as people with <1 year of data visibility before inclusion.

Follow-up

Participants in each cohort will be followed from therapy initiation date (index date). Two possibilities of analyses will be offered:

  • In a ‘fixed’ follow-up analysis, follow-up will continue until death, loss to follow-up or a pre-specified time period (e.g., 3 years) regardless of treatment duration
  • In an ‘on treatment’ analysis, follow-up will continue until treatment cessation, death, or loss of follow-up

Outcome/s

One or more study outcomes will be pre-specified, based on previous DARWIN EU algorithms or newly developed and validated ones.

In addition, a long list of negative control outcomes will be assessed, which are not known to have a causal association with the drug/s or medicinal product/s under study.

Analyses

Details will be discussed during programming of pipelines, but new users cohort analyses will include:

  • Large-scale characterisation of participants in the target and comparator cohorts, including all features available in the data before or on index date
  • Large-scale propensity scores (LSPS) will be estimated as the probability of exposure (target cohort) conditional on all available covariates available in the data with a prevalence >1%. LSPS will be estimated using Lasso regression
  • Incidence rate/s of each of the outcomes of interest in the target and comparator cohorts after LSPS matching, stratification, or inverse probability weighting
  • Diagnostic/s:
    • Covariate balance
    • Equipoise: plots of the distribution of the propensity score stratified by target vs comparator cohort
    • 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 number of negative control outcomes significantly associated with the exposure of interest
  • Rate Ratios or Hazard Ratio/s and 95% confidence intervals will be estimated using Poisson or Cox models respectively, comparing the target vs comparator (reference) cohorts after LSPS matching, stratification, or inverse probability weighting
  • Optionally, calibrated RR or HR will be estimated after empirical calibration using negative control outcomes
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