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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Sensitivity analysis for causality in observational studies for regulatory science.

Iván Díaz1, Hana Lee2, Emre Kıcıman3

  • 1Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA.

Journal of Clinical and Translational Science
|February 21, 2024
PubMed
Summary

Prespecified sensitivity analyses are vital for validating real-world data (RWD) in regulatory science. These assumption-lean methods enhance the trustworthiness of effectiveness conclusions derived from RWD studies.

Keywords:
Causal inferenceobservational datareal-world datasensitivity analysisstudy design

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Area of Science:

  • Regulatory Science
  • Data Science
  • Biostatistics

Background:

  • The 21st Century Cures Act mandates FDA guidance on using real-world evidence (RWE).
  • Stakeholders convened to establish best practices for real-world data (RWD) in regulatory science.
  • A causal roadmap for RWD study designs was recommended.

Purpose of the Study:

  • To detail the specification of sensitivity analyses for RWD studies.
  • To test the robustness of causal models against assumption violations.
  • To provide practical considerations for regulatory applications.

Main Methods:

  • An example sensitivity analysis from a Nifurtimox effectiveness study for Chagas disease.
  • Overview of various sensitivity analysis methods.
  • Emphasis on practical considerations for regulatory use.

Main Results:

  • Sensitivity analyses require careful design and prespecification to prevent bias.
  • Methods need auxiliary information and should rely on verifiable assumptions.
  • Auxiliary information must be learnable from existing scientific knowledge.

Conclusions:

  • Prespecified and assumption-lean sensitivity analyses are crucial for regulatory science.
  • These methods strengthen the validity and trustworthiness of effectiveness conclusions.
  • Robustness testing is key for reliable RWD utilization in regulatory decision-making.