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Formalized guidelines are needed for high-quality retrospective data harmonization. This study outlines a step-by-step approach to ensure rigorous, transparent, and effective data harmonization practices for research initiatives.

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Area of Science:

  • Data Science
  • Biostatistics
  • Research Methodology

Background:

  • Data harmonization is critical for efficient and accurate co-analysis of large datasets.
  • Existing retrospective data harmonization practices lack formalized, systematic guidelines for ensuring high quality.
  • Variations in terminology, procedures, and technologies hinder effective data integration.

Purpose of the Study:

  • To understand real-world data harmonization practices.
  • To facilitate the development of formal guidelines for retrospective data harmonization.
  • To provide a structured, step-by-step approach for effective data harmonization.

Main Methods:

  • Conducted a phone survey with 34 international research initiatives (2006-2015).
  • Organized expert workshops to discuss harmonization challenges and solutions.
  • Performed case studies to test and refine proposed harmonization guidelines.

Main Results:

  • Identified significant variation in methods and technologies used for retrospective harmonization.
  • Developed generic, step-by-step guidelines for data harmonization (0-5 stages).
  • Guidelines cover defining research questions, selecting studies, processing data, quality assessment, and dissemination.

Conclusions:

  • The proposed guidelines promote rigorous, effective, and transparent data harmonization.
  • Comprehensive documentation and ease of implementation are key features.
  • These guidelines aim to establish principles similar to those for systematic reviews and meta-analyses.