Background: Multinational pharmacoepidemiological studies are increasingly used to address drug safety and effectiveness questions requiring diverse population data. However, these studies pose significant challenges in study design, data harmonization, and analysis.
Objectives: To provide practical considerations for conducting multinational pharmacoepidemiology studies, with a focus on protocol development, data harmonization, statistical analysis, and reporting.
Methods: We reviewed multinational studies conducted within our collaborative networks and identified ten critical focus areas based on our experiences and common challenges encountered. We provide examples illustrating practical issues related to metadata tables, master mapping tables, follow-up time reporting, protocol harmonization, and statistical model diagnostics.
Results: Our findings highlight several key considerations to ensure the successful conduct of multinational pharmacoepidemiology studies. First, comprehensive metadata tables are essential to characterize data sources and assess feasibility issues early in the study. Second, master mapping tables help standardize coding of exposures, outcomes, and covariates to maintain consistency across databases. Third, detailed follow-up time reporting allows for a better assessment of patient retention and treatment discontinuation patterns. Additionally, a clearly defined and agreed-upon protocol ensures uniform study implementation across sites. Finally, regular diagnostic checks, including propensity score balance assessments and failure plots, are crucial for validating statistical models and maintaining comparability across datasets. Addressing these considerations systematically enhances study reliability and facilitates meaningful international comparisons.
Conclusions: Multinational pharmacoepidemiological studies require structured planning, clear communication, and proactive problem-solving to ensure robust and reliable findings. Implementing standardized approaches in protocol development, data harmonization, and statistical analysis is essential for minimizing inconsistencies and maximizing the value of real-world data.
