ENCePP Guide on Methodological Standards in Pharmacoepidemiology

13.4. 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 Guidelines for Good Pharmacoepidemiology Practices 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 epidemiology textbooks cited in the Introduction of this Guide. The Strengthening the Reporting of Observational studies in Epidemiology (STROBE) Statement Guidelines for reporting observational studies has established recommendations for improving the quality of reporting of observational studies and seeks to ensure a clear presentation of what was planned, done, and found.

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

• Poor reporting quality of observational clinical studies comparing treatments of COVID-19 - a retrospective cross-sectional study (BMC Med Res Methodol. 2022;22(1):2) found a poor reporting quality of observational studies on the treatment of COVID-19 throughout the year 2020 with a mean adherence of 45.6% to the STROBE checklist items in 147 observational studies.

• Quality of observational studies in prestigious journals of occupational medicine and health based on Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: a cross‑sectional study (BMC Res Notes 2018;11:266) found that all sub-items of the STROBE statement were reported in 63.7%, not reported in 29.7% and not applicable in 6.6% of the 60 studies evaluated.

• Chapter 11 ‘Data Collection and Quality Assurance’ of the Agency for Healthcare Research and Quality (AHRQ)’s Registries for Evaluating Patient Outcomes: A User's Guide, 4^th Edition (2020) reviews key areas of data collection, cleaning, storage, and quality assurance for registries, with practical examples.

• 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 former GPRD.

• 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.

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