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The updated Structured Process to Identify Fit-For-Purpose Data (SPIFD2) framework enhances real-world evidence generation by integrating study design and data selection. This ensures valid, transparent, and reproducible evidence for healthcare decisions.

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Area of Science:

  • Health research methodology
  • Real-world evidence generation
  • Data science in healthcare

Background:

  • Generating valid and transparent real-world evidence (RWE) is crucial for decision-making.
  • Existing frameworks like SPACE and SPIFD guide RWE study design and data selection.
  • Decision-makers require transparency in the rationale behind study design and data source choices.

Purpose of the Study:

  • To update and integrate existing frameworks (SPACE and SPIFD) into a single, enhanced process (SPIFD2).
  • To improve the generation of fit-for-purpose real-world evidence for healthcare and regulatory decisions.
  • To enhance transparency and reproducibility in RWE study design and data selection.

Main Methods:

  • The SPIFD2 framework combines previous templates into a unified process.
  • It explicitly requires articulation of the hypothetical target trial and potential sources of bias in real-world emulation.
  • References are provided to the Structured Template and Reporting Tool for Real-World Evidence (STaRT-RWE) tables for immediate use post-SPIFD2.

Main Results:

  • The SPIFD2 process mandates thorough researcher due diligence in rationalizing study design and data selection.
  • It results in stepwise documentation that supports reproducibility and clear communication.
  • The framework increases the likelihood of generating valid, fit-for-purpose RWE.

Conclusions:

  • The SPIFD2 framework provides a comprehensive, step-by-step guide for generating high-quality real-world evidence.
  • Adherence to SPIFD2 promotes transparency, reproducibility, and evidence validity.
  • This updated framework supports informed healthcare and regulatory decision-making through robust RWE.