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Sample size calculation for studies comparing binary outcomes using historical controls.

Song Zhang1, Jing Cao, Chul Ahn

  • 1Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX 75390, USA.

Biometrical Journal. Biometrische Zeitschrift
|January 22, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new sample size formula for historical control trials (HCTs). It accurately accounts for uncertainty in control group response rates, improving power and type I error control.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methodology

Background:

  • Historical control trials (HCTs) compare new therapies to controls from previous studies.
  • Existing sample size formulas, like Makuch-Simon, may not preserve desired statistical power and type I error rates.
  • Uncertainty in the historical control group's true response rate is a key challenge.

Purpose of the Study:

  • To develop a robust sample size calculation method for HCTs.
  • To address the limitations of current methods in accounting for control group variability.
  • To provide a more reliable approach for assessing experimental therapies against historical controls.

Main Methods:

  • Developed a novel sample size approach accounting for uncertainty in the historical control (HC) group's response rate.
  • Derived a closed-form sample size formula based on controlling percentiles of power and type I error.
  • Utilized simulation studies to evaluate the proposed method's performance.

Main Results:

  • Empirical power and type I error distributions from simulated HC data show significant skewness.
  • The new formula effectively controls percentiles, preserving operational characteristics under unknown HC response rates.
  • The percentile-based approach offers a more realistic assessment of HCTs.

Conclusions:

  • The proposed sample size method improves the reliability of historical control trials.
  • Controlling power and type I error percentiles provides a better understanding of HCT performance.
  • This approach offers a new perspective for the statistical assessment of HCTs.