Observational Outcome Analysis at Scale Using the OMOP Common Data Model: Large-Scale Evidence Generation with the LEGEND Framework (Motivated by the LEGEND Hypertension Studies by Suchard et al.)

The Observational Medical Outcomes Partnership Common Data Model has become a critical infrastructure for generating robust observational evidence at scale, enabling consistent analyses across heterogeneous real-world data sources. As randomized clinical trials cannot feasibly address the full breadth of comparative effectiveness and safety questions across diverse populations, standardized observational frameworks are increasingly essential for decision-relevant evidence generation. However, conventional observational studies are often limited by inconsistent data structures, variability in analytic choices, and selective reporting, which reduce reproducibility and comparability across studies and settings. This report examines large-scale observational outcome analysis using the OMOP Common Data Model, motivated by the Large-Scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) hypertension studies by Suchard et al. The LEGEND framework operationalizes high-throughput evidence generation by executing a single, pre-specified analytic protocol across multiple international databases mapped to OMOP. Key methodological components include standardized cohort definitions, propensity score adjustment to address confounding, negative control outcomes to quantify residual bias, and empirical calibration of effect estimates to improve statistical reliability. Through this lens, the report highlights how OMOP-enabled distributed analytics shift observational research from isolated, hypothesis-limited studies toward coordinated evidence ecosystems. This paradigm supports transparent, reproducible, and scalable evidence generation with direct relevance to clinical, regulatory, and policy decision-making.