The data to evidence symphony: Orchestrating research-ready data and calibrated evidence for comprehensive health insight

This thesis embarks on a transformative journey, akin to conducting an orchestra, orchestrating the symphony of evidence generation from raw data to refined insights. The thesis is divided into two sections.

The first section aims to enhance our understanding of the scientific principles that underlie data standardization, enabling the generation of evidence on a large scale More specifically, the first objective aims to uncover insights into the Extract, Transform,
and Load (ETL) process and the assessment of data quality when executed across multiple Common Data Models (CDMs). Additionally, it explores the feasibility of formulating research questions directly from these new CDMs.

The second section seeks to assess the feasibility of automating the process of identifying negative controls to calibrate analytical results when investigating causal relationships using standardized data. The focus of this section is on automating the identification
of negative controls, understanding the repercussions of erroneous selections, and investigating the practical application of negative controls chosen through automated methods in real-world scenarios.

In conclusion, this thesis encapsulates a journey through the intricacies of standardized data and calibrated evidence, revealing the foundational underpinnings essential for robust observational research.