Rethinking clinical study data: why we should respect analysis results as data

Area of Science:

• Biomedical Informatics
• Data Science
• Clinical Research Informatics

Background:

• Clinical trial development generates extensive efficacy and safety data.
• Analysis of clinical study data produces valuable results like statistics and predictions.
• These analysis results are often stored as non-machine-readable documents (e.g., PDFs), hindering further use.

Purpose of the Study:

• To propose a novel solution for managing and utilizing clinical study analysis results.
• To re-frame analysis targets from static outputs to a dynamic data model.
• To enable "calculate once, use many times" for research data.

Main Methods:

• Developing analysis results standards.
• Implementing a common data model for analysis results.
• Creating a proof-of-concept for standardization and schema construction.
• Designing a queryable schema for stored analysis results.

Main Results:

• A proposed analysis results data model that treats results as a reusable data source.
• Demonstration of a working proof-of-concept for standardization.
• A schema for storing and querying analysis results is detailed.
• The model facilitates applications beyond static reporting, including knowledge discovery.

Conclusions:

• Standardizing analysis results within a common data model enhances data reusability.
• This approach transforms static results into a dynamic, queryable data asset.
• The proposed model supports advanced applications like knowledge discovery and future analyses.