Evidence generation increasingly depends on real-world data, but access delays, sparse populations, limited comparators, and privacy constraints can leave critical evidence questions unanswered.
Synthetic data offers a way to reduce these barriers by creating statistically representative datasets that support faster analysis, testing, collaboration, and bias assessment. Its value, however, depends on rigorous validation, clear governance, and the intended context of use.
Download The Assistance Era’s Structural Limit Beyond Real-World Data: The Role of Synthetic Data in Evidence Generation to explore where synthetic data adds value, its limitations, and how a tiered, risk-based approach supports credible and responsible evidence generation.
