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Sample size calculation for a proof of concept study.

Yin Yin1

  • 1GlaxoSmithKline, Research Triangle Park, NC 27709, USA. yy0408@gsk.com

Journal of Biopharmaceutical Statistics
|November 5, 2002
PubMed
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Calculating sample size for proof of concept (PoC) studies is crucial for pharmaceutical decision-making. This research introduces a Bayesian method to determine the necessary information and sample size for effective PoC studies.

Area of Science:

  • Clinical Trials
  • Bayesian Statistics
  • Pharmaceutical Research

Background:

  • Sample size calculation is critical for confirmatory clinical trials to meet regulatory standards for Type I error (typically <0.05).
  • The importance of sample size calculation for internal pharmaceutical decision-making studies, such as proof of concept (PoC), is often overlooked.

Purpose of the Study:

  • To introduce a Bayesian methodology for sample size calculation in proof of concept (PoC) studies.
  • To identify essential information needed for planning PoC studies and their sample size determination.

Main Methods:

  • A Bayesian approach is presented for sample size calculation in PoC studies.
  • The method focuses on identifying key information required for planning and executing PoC studies.

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Main Results:

  • The study explores the relationships between regulatory requirements, the probability of meeting these requirements, PoC goalposts, and the sample size.
  • Results illustrate how Bayesian methods can inform sample size decisions for PoC studies.

Conclusions:

  • A Bayesian method provides a framework for sample size calculation in PoC studies, addressing a gap in current practices.
  • This approach aids pharmaceutical companies in making informed internal decisions through rigorous PoC study planning.