Lessons Learned from PROTECT on Common Protocols for Multi-Database StudiesDatabases

  • Insights into common protocol studies – including a list of recommendations – which were conducted in the framework of the PROTECT project.
  • Presented by: Olaf Klungel, Utrecht Universiteit
  • Presented at: ENCePP Plenary Meeting, 24 November 2015


  • To Strengthen the monitoring of benefit-risk of medicines in Europe by developing innovative methods
  • To enhance early detection and assessment of adverse drug reactions from different data sources (clinical trials, spontaneous reporting and OBSERVATIONAL STUDIES)
  • To enable the integration and presentation of data on benefits and risks

These methods are being tested in real-world situations.

Main Objectives

  • Explain differences in drug-adverse event associations due to choices in methodology and databases
  • Replication program of studies
    • Same study EU database
    • Different study database, specifically US data source
(Abbing-Karahagopian V, et al. Curr Clin Pharmacol 2014;9:130-138)


  • Common protocol for each drug-AE pair
  • Extensive sensitivity analyses on main methodological issues
  • Common standards, templates, procedures
  • Detailed data specification including definitions of exposures, outcomes, and confounders for each database.
  • Blinding of results of in-parallel and replication analyses
  • Stepwise unblinding after completion of each design
  • Registration of protocols at ENCePP to guarantee transparency


Challenges/ Lessons Learned

  • Substantial time (+/-1 year) reach agreement on common protocol – Consensus/buy-in on approach between stakeholders!
  • Research question determined choice of database and design of data collection
  • Detailed data-specification documents are needed to harmonize procedures and analyses
  • Frequent communication between research centres to reduce variation in “interpretation” of protocol
  • During programming and analysis phase further clarification is required and needs documentation

Methodological Determinants of Drug-AE Associations

  • Databases
  • Study design
  • Outcome definition
  • Exposure definition
  • Methods to control for confounding


  • Develop common protocol with great detail to reduce methodological differences and “interpretation” by researchers
  • Solid infrastructure fo rcommunication/collaboration
  • Conduct analysis in parallel in multiple DBs versus “apriori” pooling of DBs
  • Cherish heterogeneity and explore its sources
  • To test robustness of findings conduct multiple sensitivity analyses:
  • Multiple designs (e.g. Cohort/case-control vs case-only)
  • Exposure (e.g. Individual AEDs), outcome (e.g SUI), confounding adjustment
  • Replication needed if parallel analysis consistent?

What’s Next?

  • Network for observational safety and effectiveness studies
    • Common protocol in multiple databases may increase confidence in investigations
    • Testing of existing network
      • New safety signals
      • Platform for methods development and testing
    • Further development of network infrastructure • Library of codes/programs
      • Governance of network
      • Structure for collaboration/communication
      • Collaboration with other networks

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