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Sample size calculation for the one-sample log-rank test.

René Schmidt1, Robert Kwiecien, Andreas Faldum

  • 1Institute of Biostatistics and Clinical Research, University of Münster, Münster, 48149, Germany.

Statistics in Medicine
|December 16, 2014
PubMed
Summary

This study introduces an improved sample size calculation for the one-sample log-rank test, crucial for comparing single treatment groups against historical controls in clinical trials. A new stopping criterion is proposed, enhancing power and controlling Type I error rates, especially in smaller trials.

Keywords:
one-sample log-rank testphase-II trialpower calculationsample size

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Area of Science:

  • Biostatistics
  • Clinical Trial Design

Background:

  • The one-sample log-rank test is valuable for comparing a single treatment group's survival curve against a historical control.
  • This scenario is common in Phase II clinical trials using survival endpoints to assess new treatments.

Purpose of the Study:

  • To provide an improved method for sample size calculation for the one-sample log-rank test.
  • To introduce and evaluate a novel stopping criterion for trials using this test.

Main Methods:

  • The study proposes a new stopping criterion for sample size calculation in one-sample log-rank tests.
  • Asymptotic equivalence between the new and traditional methods (event-based) is demonstrated.
  • A simulation study was conducted to compare the performance of both criteria, particularly for small sample sizes.

Main Results:

  • Traditional sample size formulas based on the number of events can lead to underpowered trials and inflated Type I error rates.
  • The newly proposed stopping criterion maintains statistical power and controls the Type I error rate effectively.
  • Simulations indicate the new criterion is preferable for planning trials, especially with small sample sizes.

Conclusions:

  • The proposed stopping criterion offers a more reliable approach to sample size calculation for the one-sample log-rank test.
  • This improvement is particularly significant for ensuring adequate power and controlled error rates in clinical trials with survival endpoints.