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Discovery reliability.

Anders Hånell1

  • 1Department of Neuroscience, The Karolinska Institute, Stockholm, Sweden.

Journal of Cerebral Blood Flow and Metabolism : Official Journal of the International Society of Cerebral Blood Flow and Metabolism
|March 15, 2019
PubMed
Summary

Trusting experimental results, especially in pre-clinical drug studies, is crucial. Key factors like pre-study likelihood and statistical power quantify result reliability, aiding research design and discovery reliability.

Keywords:
Animal modelsanimal studiesbiostatisticsclinical trial designmathematical modeling

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Area of Science:

  • Pharmacology and Toxicology
  • Biostatistics
  • Neuroscience Research

Background:

  • Pre-clinical drug studies are critical for neurological disease research.
  • Frequent failures in Phase III clinical trials raise concerns about the reliability of pre-clinical findings.
  • The trustworthiness of experimental outcomes requires careful evaluation.

Purpose of the Study:

  • To assess the reliability of experimental results in pre-clinical drug studies.
  • To introduce quantifiable factors for evaluating discovery reliability.
  • To guide the design of more dependable pre-clinical research.

Main Methods:

  • Investigated the impact of pre-study likelihood of treatment benefit on result reliability.
  • Analyzed the role of statistical power in determining the trustworthiness of experimental outcomes.
  • Developed a framework for quantifying discovery reliability.

Main Results:

  • Identified pre-study likelihood and statistical power as key determinants of result reliability.
  • Demonstrated a quantifiable method to assess the extent to which a study result can be trusted.
  • Provided insights into improving the design of pre-clinical studies.

Conclusions:

  • The reliability of pre-clinical drug study results can be enhanced by considering pre-study likelihood and statistical power.
  • Quantifying discovery reliability is essential for advancing neurological disease research.
  • This approach aids in designing more robust and trustworthy pre-clinical experiments.