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Related Experiment Videos

Comparing sample size formulae for trials with unbalanced allocation using the logrank test.

F Y Hsieh1

  • 1Computing and Statistical Services, Anaquest Inc., BOC Health Care, Murray Hill, NJ 07974.

Statistics in Medicine
|June 15, 1992
PubMed
Summary
This summary is machine-generated.

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This study on sample size calculations for unbalanced designs found that Freedman's formula best predicts logrank test power when group sizes are unequal. Equal sample sizes may not maximize statistical power.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Accurate sample size calculation is critical for the statistical power of clinical trials.
  • Existing formulas (Schoenfeld, Freedman, Hsieh, Shuster) for unbalanced designs may yield different power predictions.
  • The logrank test is a common statistical method for analyzing survival data.

Purpose of the Study:

  • To compare the accuracy of different sample size formulas for unbalanced designs in survival analysis.
  • To evaluate the impact of sample size ratios on the power of the logrank test.
  • To determine which formula best predicts statistical power under various allocation scenarios.

Main Methods:

  • Monte Carlo simulations were employed to assess the power of the logrank test.

Related Experiment Videos

  • The study simulated various sample size ratios between two groups.
  • The accuracy of Schoenfeld, Freedman, Hsieh, and Shuster formulas was evaluated.

    • Main Results:

    • Freedman's formula accurately predicted logrank test power when the sample size ratio approached the reciprocal of the hazard ratio.
    • Schoenfeld's formula provided accurate power predictions for equal sample sizes.
    • The power curve of the logrank test demonstrated relative flatness across a range of sample size ratios.

    Conclusions:

    • Equal sample size allocation does not always maximize the power of the logrank test in unbalanced designs.
    • Freedman's formula is recommended for unbalanced designs when sample size ratios are known or can be estimated.
    • Schoenfeld's formula remains a reliable choice for predicting power with equal group sizes.