Overview: Real-world observational data is inherently prone to confounding, misclassification, and bias. OHDSI’s methods research rigorously evaluates, benchmarks, and develops epidemiological and statistical techniques to determine which analytical designs produce trustworthy scientific findings.

Value to OHDSI: High data quality alone does not guarantee correct answers. Empirical methods research ensures that OHDSI studies minimize false positives and generate reliable, reproducible real-world evidence.

Key Resources:

• Empirical Calibration Framework: Negative and positive control synthesis for residual systematic error adjustment.

• Method Evaluation Benchmarks: Large-scale synthetic and empirical datasets assessing propensity score approaches, self-controlled case series (SCCS), and counterfactual designs.

• Methods Library: Publications, study protocols, and mathematical tutorials documenting causal inference frameworks.

What OHDSI Has Done:

• Published foundational benchmarking studies demonstrating that commonly used observational study designs often suffer from high error rates if not empirically calibrated.

• Pioneered large-scale negative control methodology to adjust p-values and confidence intervals for unmeasured confounding.

• Developed standardized frameworks for patient-level predictive modeling and counterfactual comparative cohort designs.