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Area of Science:

  • Toxicology
  • Statistical Analysis
  • Regulatory Science

Background:

  • Traditional significance testing using p-values and fixed thresholds (e.g., 5% false positive rate) is increasingly discouraged.
  • Dichotomization of results into significant/non-significant hinders nuanced interpretation.
  • Alternative statistical approaches are needed for reliable scientific conclusions.

Purpose of the Study:

  • To advocate for the use of effect sizes and compatibility intervals as a superior alternative to traditional significance testing.
  • To demonstrate the application of this concept in regulatory toxicology for hazard and safety evaluations.
  • To provide practical examples and open-source software for implementing these methods.

Main Methods:

  • Utilized effect size interpretation with compatibility intervals (confidence intervals) to assess assay results.
  • Applied the concept to regulatory toxicology, focusing on proof of hazard and proof of safety.
  • Developed and provided R software code for statistical computing to facilitate the application of these methods.

Main Results:

  • Demonstrated the applicability of effect sizes and compatibility intervals through three case studies.
  • Case studies involved multiple endpoints, multiple statistical models, and the incorporation of historical control data.
  • The proposed method offers a more robust framework for evaluating toxicological assays.

Conclusions:

  • The interpretation of effect sizes and their compatibility intervals provides a more informative approach than traditional significance testing.
  • This methodology is suitable for complex toxicological evaluations, including proof of hazard and safety.
  • Open-source R code is available to support the adoption of these advanced statistical practices in toxicology.