PhD defence M.S. (Clair) Blacketer

Below is a brief summary of the dissertation:

There is a fundamental understanding that the reliability of real-world evidence depends on the quality and fitness of the underlying data. This thesis focuses on improving the foundational science of data standardization, data quality, and fitness-for-purpose in federated networks to increase trust in the evidence generated. This work demonstrated that: i) data standardization offers a mechanism to implement consistent approaches to address assumptions about underlying populations of databases, ii) systematic evaluation of data quality can be scaled across federated networks through the use of standard tools to enable transparent and consistent assessment of data conformance, completeness, and plausibility, and iii) reliable fit-for-purpose assessments can be conducted in federated networks using precomputed summary statistics to preserve patient privacy and decrease resource expenditure in the early study phase. In conclusion, this thesis argues that advancing the science of data standardization, data quality, and fitness-for-purpose is integral to the generation of reliable evidence to support actionable insights that improve patient care.