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

Jianrong Wu1

  • 1Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.

Pharmaceutical Statistics
|October 24, 2014
PubMed
Summary
This summary is machine-generated.

This study derives an exact variance for the one-sample log-rank test under the alternative hypothesis. The proposed sample size formula ensures adequate statistical power for survival studies comparing a single sample to a standard population.

Keywords:
counting processone-sample log-rank testsample size formulastandard populationtime to event

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Area of Science:

  • Biostatistics
  • Survival Analysis

Background:

  • The one-sample log-rank test is crucial for comparing survival data.
  • Accurate sample size calculation is essential for study power and reliability.

Purpose of the Study:

  • To derive the exact variance of the one-sample log-rank test statistic under the alternative hypothesis.
  • To propose a novel sample size formula based on this exact variance.
  • To evaluate the performance of the proposed formula in survival study design.

Main Methods:

  • Derivation of the exact variance for the one-sample log-rank test statistic.
  • Development of a sample size formula utilizing the derived exact variance.
  • Monte Carlo simulations to assess the power of studies designed with the proposed formula.

Main Results:

  • An exact variance for the one-sample log-rank test statistic under the alternative hypothesis was successfully derived.
  • A new sample size formula was proposed, directly incorporating the exact variance.
  • Simulation results demonstrated that the proposed formula yields adequate statistical power for comparative survival studies.

Conclusions:

  • The derived exact variance and proposed sample size formula offer a statistically sound method for designing survival studies.
  • This approach enhances the ability to detect differences in survival between a single sample and a standard population.
  • The findings support the use of the proposed formula for robust sample size determination in clinical research.