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Data management and data analysis techniques in pharmacoepidemiological studies using a pre-planned multi-database
Marloes T Bazelier1, Irene Eriksson2, Frank de Vries1,3
1Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Netherlands.
Pharmacoepidemiology and Drug Safety
|July 16, 2015
Summary
Pharmacoepidemiological multi-database studies enhance drug safety research by pooling data. Consistent reporting of data management and analysis is crucial for accurate interpretation of findings.
Area of Science:
- Pharmacoepidemiology
- Health Research Methods
Background:
- Multi-database studies are increasingly used in pharmacoepidemiology.
- These studies combine data from multiple sources to increase statistical power and generalizability.
- However, variations in data management and analysis techniques can impact results.
Purpose of the Study:
- To identify pharmacoepidemiological multi-database studies.
- To describe the data management and data analysis techniques employed in these studies.
Main Methods:
- Systematic literature searches in PubMed and Embase, supplemented by manual searches.
- Inclusion of studies published from 2007 onwards that involved pre-planned data combination for analysis.
- Extraction of information on study characteristics, individual-level and meta-analysis methods, data management, and study motivations.
Main Results:
- Twenty-two pharmacoepidemiological multi-database studies were included, using 2 to 17 databases.
- Cohort designs (82%) were more common than case-control designs (18%).
- Logistic regression was the most frequent individual-level analysis (41%), and individual patient data meta-analysis was common (73%).
- Reporting on central programming and heterogeneity assessment was often incomplete.
Conclusions:
- Pharmacoepidemiological multi-database studies are a powerful and popular strategy for drug safety research.
- Standardized reporting of database management and analysis methods, including central programming and heterogeneity testing, is essential for reliable interpretation.
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