Study type

Study topic

Human medicinal product
Disease /health condition

Study type

Non-interventional study

Scope of the study

Assessment of risk minimisation measure implementation or effectiveness

Data collection methods

Secondary use of data
Non-interventional study

Non-interventional study design

Cohort
Study drug and medical condition

Anatomical Therapeutic Chemical (ATC) code

(A02BC) Proton pump inhibitors
Proton pump inhibitors

Medical condition to be studied

Acute myocardial infarction
Ischaemic stroke
Population studied

Short description of the study population

All patients aged 18 years or older who have been insured by the statutory health insurance provider Allgemeine Ortskrankenkasse (AOK) Bayern, for at least 2 years since January 2008.
Eligibility/exclusion criteria are: age of at least 18 years; no prevalent cardiovascular disease; no prior use of any PPIs. Additionally we demand no prior use of the
comparator medication in target trial.

Age groups

  • Adult and elderly population (≥18 years)
    • Adults (18 to < 65 years)
      • Adults (18 to < 46 years)
      • Adults (46 to < 65 years)
    • Elderly (≥ 65 years)
      • Adults (65 to < 75 years)
      • Adults (75 to < 85 years)
      • Adults (85 years and over)

Estimated number of subjects

6100000
Study design details

Main study objective

We investigate whether long-term intake of proton pump inhibitors is associated with the risk of myocardial infarction and stroke.

Outcomes

The risk of myocardial infarction and stroke associated with the long-term intake of proton pump inhibitors.

Data analysis plan

|| Estimation of the observational analog of the intention-to-treat effect: We will fit pooled logistic regression models to estimate hazard ratios and survival curves using time-varying stabilized inverse-probability weights. || Estimation of the observational analog of the per-protocol effect: We will apply different prespecified dose-response models, and derive another dose-response model from the data estimating a weighted cumulative exposure model. For each of these models we fit marginal structural Cox models using stabilized inverse probability of treatment weights.