Assessing Fitness-for-Purpose Across Data Sources

Choosing the right real-world data source can make or break your study, yet data quality assessment is often an afterthought. This video discusses how to determine whether a dataset is truly fit for purpose before your research begins, so your evidence holds up when it matters most.

In this video, you’ll learn:

• How to identify common real-world data quality problems — including coding variability, gaps in patient follow-up, and misinterpreted missing data — before they compromise your results
• A practical framework for selecting the right data source for your study, including the critical questions to ask yourself and your data partners upfront
• Why regulators and HTAs increasingly prioritize data fitness over sample size — and how documenting your data selection process strengthens regulatory confidence in your findings