ENCePP Guide on Methodological Standards in Pharmacoepidemiology

12.4. Quality management in observational studies

Quality management principles applicable to observational studies with primary data collection or secondary use of data are described in the Commission Implementing Regulation (EU) No 520/2012, GVP Module I, FDA’s Best Practices for Conducting and Reporting Pharmacoepidemiologic Safety Studies Using Electronic Health Care Data Sets, in recommendations from scientific societies such as the ISPE GPP or the Guidelines and recommendations for ensuring Good Epidemiological Practice (GEP): a guideline developed by the German Society for Epidemiology (Eur J Epidemiol. 2019;34(3):301-17) and in general textbooks of epidemiology cited in the Introduction chapter of this Guide.

Chapter 15.7 (Real-world evidence and pharmacoepidemiology) discusses recommendations issued by regulatory authorities as regards the quality of studies based on observational data conducted for regulatory purposes in terms of, e.g., reliability of data sources, relevance, study designs and transparency.

The following articles are practical examples of quality aspects implementation in different settings:

• Quality Assurance and Quality Control in Longitudinal Studies (Epidemiol Rev. 1998,20(1);71-80) provides a comprehensive overview of components of QA and QC in multi-centre cohort studies with primary data collection that should be an integral part of their design. Training, quality assurance, and assessment of medical record abstraction in a multisite study (Am J Epidemiol. 2003;157:546-51) describes a practical approach to assurance of good quality control in a large multi-site study.

• Quality assurance in non-interventional studies (Ger Med Sci. 2009;7:Doc 29: 1-14) proposes measures of quality assurance that can be applied at different stages of non-interventional studies without compromising the character of non-intervention.

• Chapter 11 ‘Data Collection and Quality Assurance’ of the AHRQ Registries for Evaluating Patient Outcomes: A User's Guide, 4^th Edition, reviews key areas of data collection, cleaning, storing, and quality assurance for registries, with practical examples.

• Interviewer variability – quality aspects in a case–control study (Eur J Epidemiol. 2006;21(4);267-77) describes the procedures used to reduce interviewer variability, including procedures of quality assurance (i.e. education and training of interviewers and data validity checks) and quality control (i.e. a classification test, annual test interviews, expert case validation and database validation).

• Establishment of the nationwide Norwegian Prescription Database (NorPD) – new opportunities for research in pharmacoepidemiology in Norway (Norsk epidemiologi. 2008;18(2):129-36) describes the quality checks applied to the database.

• Validation and validity of diagnoses in the General Practice Research Database (GPRD): a systematic review (Br J Clin Pharmacol. 2010;69:4-14) assesses the quality of the methods used to validate diagnoses in the GPRD.

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