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Updated: Aug 22, 2025

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

Joana M Barros1,2, Lukas A Widmer3, Mark Baillie4

  • 1Analytics, Novartis Pharma AG, Basel, Switzerland. joanamarquesbarros@gmail.com.

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|November 10, 2022
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Summary
This summary is machine-generated.

Analysis results from clinical studies are often lost in unreadable formats. We propose a new data model to standardize and reuse these valuable analysis results for better knowledge discovery.

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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.