Study type

Study type

Not applicable

Scope of the study

Assessment of risk minimisation measure implementation or effectiveness
Effectiveness study (incl. comparative)
Safety study (incl. comparative)

If ‘Not applicable’, further details on the study type

Active surveillance, Sequential matched cohort study
Study drug and medical condition

Study drug International non-proprietary name (INN) or common name

LINAGLIPTIN

Medical condition to be studied

Type 2 diabetes mellitus
Population studied

Age groups

Adults (18 to < 46 years)
Adults (46 to < 65 years)
Adults (65 to < 75 years)
Adults (75 to < 85 years)
Adults (85 years and over)

Estimated number of subjects

120000
Study design details

Main study objective

To conduct a multi-year research surveillance program that establishes and periodically updates initiator cohorts of linagliptin, within-class comparators (saxagliptin, sitagliptin, alogliptin), and out-of-class comparators (glitazones, 2nd generation of sulfonylureas (SUs)), and longitudinally follow them for the incidence of a variety of health outcomes.

Outcomes

- Acute myocardial infarction- Coronary revascularization- Hemorrhagic stroke- Hospitalization for acute coronary syndrome- Ischemic stroke- Major adverse cardiovascular event (composite of coronary revascularization, hospitalization for acute coronary syndrome, ischemic and hemorrhagic stroke), - Heart failure hospitalization - Incident End-Stage Renal Disease (ESRD) - Acute renal failure (ARF)- ARF that requires dialysis

Data analysis plan

We will receive new data as they become available on a periodic basis (every 6 months) and, at each data cut, we will update the original set of data, form sequential cohorts by propensity score (PS) matching within 6-month blocks of time, follow patients for each of the outcomes of interest in a prospective manner, and estimate measures of effect using person-time based analyses among patients who initiate linagliptin versus a comparator drug. Unadjusted and adjusted relative risks (hazard ratios) and rate differences will be estimated. In adjusted analyses, we will use propensity score (PS) matching to balance potential confounders.