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Related Experiment Videos

Uniform power method for sample size calculation in historical control studies with binary response.

J J Lee1, C Tseng

  • 1Department of Biostatistics, University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA. jjlee@mdanderson.org

Controlled Clinical Trials
|August 22, 2001
PubMed
Summary
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Makuch and Simon's sample size formula for historical control (HC) studies overestimates savings. A new uniform power method provides accurate sample size calculations for HC trials, ensuring desired statistical power even with response rate uncertainty.

Area of Science:

  • Biostatistics
  • Clinical Trial Design

Background:

  • Historical control (HC) studies offer potential sample size savings but rely on assumptions about control group response rates.
  • Existing formulas, like Makuch and Simon's, assume the observed control response rate is the true rate, potentially leading to underpowered studies.

Purpose of the Study:

  • To evaluate the performance of Makuch and Simon's sample size formula under uncertainty in the historical control response rate.
  • To develop a new method for calculating sample sizes in HC studies that accounts for uncertainty in the control response rate and ensures precise statistical power.

Main Methods:

  • Calculated expected power and sample size under an "omniscient model" to assess Makuch and Simon's procedure.
  • Developed a "uniform power method" to determine the experimental group sample size needed for exact power, irrespective of the estimated HC response rate.

Related Experiment Videos

  • Compared the new method's sample size requirements with Makuch and Simon's and the randomized design formula.

    • Main Results:

    • Makuch and Simon's method yields lower power than specified when there is uncertainty in the HC response rate.
    • Even the larger sample size from the randomized design formula does not guarantee advertised power in the HC setting.
    • The new uniform power method provides correct, predictable power across permissible response rates and results in sample sizes closer to those for randomized designs.

    Conclusions:

    • The perceived sample size savings in HC studies are often unjustified without the strong assumption of the observed HC rate being the true rate.
    • HC designs can be viable if biases are minimized, but results require confirmation with concurrent controls.
    • The developed uniform power method offers a more reliable approach to sample size calculation in HC trials.