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Standardised and Reproducible Phenotyping Using Distributed Analytics and Tools in the Data Analysis and Real World

Francesco Dernie1,2, George Corby1,2, Abigail Robinson1,2

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Summary

A new framework ensures reliable disease phenotype generation from real-world data for observational studies. This method, applied to pancreatic cancer and systemic lupus erythematosus, enhances data analysis for regulatory purposes.

Keywords:
pancreatic cancerphenotypingsystemic lupus erythematosus

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Area of Science:

  • Health Informatics
  • Observational Studies
  • Real-World Data Analysis

Background:

  • Reliable disease phenotypes are crucial for accurate observational study findings.
  • The Data Analysis and Real-World Interrogation Network (DARWIN EU) requires robust phenotyping methods.
  • Standardized approaches are needed for generating phenotypes from real-world data.

Purpose of the Study:

  • To present a reproducible framework for generating reliable and traceable disease phenotypes using real-world data.
  • To facilitate phenotype generation within the DARWIN EU network.
  • To demonstrate the framework's application for pancreatic cancer and systemic lupus erythematosus (SLE).

Main Methods:

  • A 14-step phenotyping process based on a standard operating procedure was developed.
  • Bespoke R packages were used for codelist generation and review.
  • Real-world data mapped to the OMOP Common Data Model was utilized.

Main Results:

  • Codelists for pancreatic cancer and SLE were successfully generated.
  • Cohorts were created in six OMOP-mapped databases, showing comparable incidence/prevalence to literature.
  • Generated cohorts exhibited characteristics consistent with clinical expectations.

Conclusions:

  • The developed phenotyping process, using bespoke tools, enables comprehensive codelist generation and cohort analysis.
  • Structured and reproducible phenotyping is essential for the reliability of observational studies, particularly for regulatory decision-making.
  • This framework supports large-scale exploration of cohort characteristics.