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[Development of a pre-processing workflow for real world data derived from multicenter clinical laboratories].

C Liu1, X X Peng1, S Y Cai2

  • 1Center for Clinical Epidemiology and Evidence-based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China Hainan Institute of Real World Data, Qionghai 571400, China.

Zhonghua Liu Xing Bing Xue Za Zhi = Zhonghua Liuxingbingxue Zazhi
|February 18, 2025
PubMed
Summary

A new workflow standardizes real-world data (RWD) from multiple clinical labs. This improves data quality and generates more reliable real-world evidence (RWE) for research.

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Area of Science:

  • Clinical Laboratory Science
  • Biostatistics
  • Epidemiology

Context:

  • Real-world data (RWD) from multicenter clinical laboratories presents standardization challenges.
  • Robust real-world evidence (RWE) generation requires high-quality, pre-processed RWD.
  • Existing RWD lacks a standardized pre-processing methodology.

Purpose:

  • To develop a comprehensive pre-processing workflow for RWD from multicenter clinical laboratories.
  • To enhance data standardization and improve the robustness of real-world evidence (RWE).
  • To provide a methodological reference for controlling systematic errors in multicenter RWD.

Summary:

  • A workflow was developed through expert interviews and thematic analysis.
  • Key steps include variable list development, quality assessment, data cleaning, heterogeneity analysis, and targeted pre-processing.
  • The workflow addresses data inconsistencies across different clinical laboratories.

Impact:

  • Establishes a standardized methodology for pre-processing multicenter clinical laboratory RWD.
  • Enhances the validity and reliability of real-world evidence (RWE).
  • Facilitates more robust and accurate clinical research utilizing RWD.