Choosing real-world data for clinical and epidemiological research: methodological lessons from NHIRD and TriNetX—A narrative review

INTRODUCTION: Large-scale real-world data (RWD) are increasingly used in clinical and epidemiological research, although database-specific structures and limitations may affect study validity and applicability. The Taiwan National Health Insurance Research Database (NHIRD) and the TriNetX network are two widely used RWD sources. This review compares their key features, strengths, and limitations and discusses approaches to address methodological challenges in real-world studies. DISCUSSION: The NHIRD comprises comprehensive, population-based, longitudinal claims data covering nearly the entire Taiwanese population. Its strengths include minimal selection bias and broad follow-up capacity. However, limitations include infrequent updates, limited clinical detail, and a Taiwan-specific context that may restrict generalizability. In contrast, TriNetX is a multinational federated network of electronic medical records from diverse healthcare systems, offering larger and more heterogeneous populations, richer clinical variables, and near real-time analytic capability, but with potential hospital-based selection bias and limited flexibility due to its fixed analytic interface. Representative studies published between 2010 and 2024 demonstrate the application of both databases across multiple medical disciplines. To mitigate data-related limitations, commonly used strategies include refined inclusion and exclusion criteria, proxy variables for unavailable measures, and triangulation with external datasets, which can strengthen study validity and interpretability. CONCLUSIONS: NHIRD and TriNetX are complementary real-world data sources, each with distinct strengths and limitations. Aligning research objectives with database characteristics is essential for appropriate study design. Recognition of platform-specific trade-offs and application of targeted methodological strategies support the validity and generalizability of real-world evidence.