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A comprehensive open-source library for exact required sample size in binary clinical trials.

Chris J Lloyd1, Enrico Ripamonti2

  • 1Melbourne Business School, The University of Melbourne, Melbourne, Australia.

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Summary
This summary is machine-generated.

This study introduces a new R library for precise sample size calculations in randomized controlled trials (RCTs). It addresses limitations of approximate formulas, offering exact solutions for improved trial design and statistical power.

Keywords:
Exact computationR libraryRCTSample size

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Software Development

Background:

  • Accurate sample size determination is critical for randomized controlled trials (RCTs) to ensure desired statistical power and control Type I error rates.
  • Existing approximate formulas for binary trials often fail to meet target significance levels (e.g., 5%) and power (e.g., 80%), especially with larger sample sizes.
  • Standard methods may violate desired operating characteristics, and adjustments for size bias can significantly reduce statistical power.

Purpose of the Study:

  • To develop a comprehensive R library for the exact sample size determination of RCTs.
  • To overcome computational challenges associated with calculating exact power and sample sizes for exact and quasi-exact tests.
  • To provide an open-source solution for accurate and efficient sample size calculations in clinical trial planning.

Main Methods:

  • Development of an R library employing innovative statistical approaches to address computational intractability.
  • Implementation of methods to accurately compute exact power and size for various test outcomes.
  • Strategies for efficiently searching minimum sample sizes that meet predefined statistical targets.

Main Results:

  • The library aims to provide exact sample size calculations, overcoming the limitations of approximate methods.
  • It addresses the computational infeasibility of exact calculations for large sample sizes and small effect sizes.
  • The developed solutions enable accurate determination of sample sizes crucial for trial protocols.

Conclusions:

  • The new R library offers a robust solution for exact sample size determination in RCTs.
  • It democratizes access to precise sample size calculations through an open-source release.
  • This tool will enhance the rigor and reliability of clinical trial design and statistical analysis.