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Related Experiment Videos

Likelihood and clinical trials.

G Hill1, W Forbes, J Kozak

  • 1Research Department, Sisters of Charity of Ottawa Health Services Inc., 43 Bruyere Street, Ottawa, Ontario, Canada.

Journal of Clinical Epidemiology
|April 13, 2000
PubMed
Summary

This review examines the evolution of statistical analysis in clinical trials, highlighting the shift from traditional significance testing and decision theory towards Bayesian and likelihood approaches. It explores the ongoing debate shaping modern statistical practices.

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Statistical Theory

Background:

  • The historical application of statistical theory in clinical trial analysis is reviewed.
  • Current practices represent a hybrid of significance testing and decision theory.
  • This established methodology faces challenges from alternative statistical paradigms.

Observation:

  • The dominant statistical approach in clinical trials is a blend of Edgeworth, Fisher, and Neyman-Pearson-Wald theories.
  • Bayesian statistics presents a significant challenge to the current orthodoxy.
  • The likelihood approach, originating from Fisher and Barnard, offers a distinct alternative.

Findings:

  • The review critically assesses the historical development and current state of statistical analysis in clinical trials.

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  • It identifies the tension between traditional methods and emerging Bayesian and likelihood frameworks.
  • Hypothetical data from Bradford Hill's Lancet series illustrates the likelihood approach.

    • Implications:

    • Understanding these statistical evolution is crucial for interpreting clinical trial results accurately.
    • The rise of Bayesian and likelihood methods may reshape future clinical trial design and analysis.
    • This work provides a historical context for ongoing debates in biostatistics and evidence-based medicine.