Study Type

Trend analyses and RMM effectiveness are considered complex DUS as they require bespoke modelling of interventions like risk minimization measures (RMM) after the completion of population- and/or patient-level DUS. These will typically take the form of interrupted time series to analyse the impact of a regulatory action on the use of a medicine at the population-level (e.g., incidence of use before vs after RMM) and/or at the patient level (e.g., characteristics of new drug users before vs after RMM).

Study Design

Population-level cohort and New drug user cohort

Participant/s

Population-level analyses will include the entire source population  with at least some time (typically 1 year) of data visibility available before start of study period. Additional eligibility criteria could apply as in population-level DUS (see above).

Patient-level analyses will be restricted to new or prevalent users of a specified list of medicine/s or medicinal product/s during a specified time point/period, using a washout, and with potential additional eligibility criteria considered (see above).

Exposure/s

At least one RMM will have been imposed at a known date in time. This RMM (or series of RMMs) will be the main study exposure/intervention for analysis. The study period will therefore be pre-specified, and divided into before (i.e., unimpacted time) and after (i.e., impacted) calendar time.

A lag period between the publication or communication of the RMM and the start of the post-exposure study period could be considered to allow for the RMM to have an impact on the study outcome/s.

Outcome/s

The following will be estimated for a pre-specified study period, including at least 1 year before and after the RMM exposure/s of interest:

• Population-based incidence rates of use of a drug/drug class over time
• Population-based prevalence of use of a drug/drug class over time

At the patient level, two cohorts of new or prevalent drug user/s will be studied, namely those who initiated or were users of the treatment of interest in the period before (unimpacted) vs after the RMM (impacted). The following outcome/s will be obtained and compared for both cohorts:

• New drug user cohort/s patient-level characteristics on or before index date
• (Optionally) prevalent drug user cohort/s patient-level characteristics on or before index date

Follow-up

For population-level analyses, follow-up will start on a pre-specified calendar time point, at least 1 year before the imposed RMM, and will continue for at least 1 year after it.

For patient-level analyses, follow-up will go from the date of therapy initiation (for new users) or a pre-specified date (for prevalent users) until the earliest of loss to follow-up, end of data availability, or death. Patients might be censored at the time they discontinue treatment or switch to an alternative therapy, or at the date of RMM.

Analyses

Incidence and prevalence rate/s of drug/s use over time will be estimated as detailed in section 2.1. Once these are available, segmented regression methods will be used to estimate the impact of the imposed RMM/s on population-based pre- vs post-intervention trends of drug/s use. Coefficients for the segmented regression indicating the difference in trend between the periods and immediately after the intervention (step change) will be reported to quantify as a formal test of the impact of the RMM/s on the incidence and prevalence of use, together with Durbin-Watson residuals as a diagnostic. In case of autocorrelation, ARIMA/X models will be fitted instead of segmented regression, if data permits.

For patient-level analyses, standardised mean differences of each of the covariates for the comparison between new/prevalent drug users in the pre-RMM vs post-RMM period will be obtained as a measure of the impact of the RMM on the profile of new drug users. Additionally, measures of patient-level DUS will be provided, stratified by time of therapy initiation pre or post RMM.