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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Sample size calculation for testing differences between cure rates with the optimal log-rank test.

Jianrong Wu1

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

Journal of Biopharmaceutical Statistics
|February 17, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces new sample size calculations for comparing cure rates between two groups. The optimal log-rank test proves more efficient, especially with low cure rates and censoring.

Keywords:
Clinical trialcure modellog-rank testoptimal testsample size

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Comparing cure rates in two groups is crucial for clinical trials.
  • Existing methods may lack efficiency, particularly in specific scenarios.

Purpose of the Study:

  • To develop sample size calculations for comparing cure rates.
  • To evaluate the efficiency of the optimal log-rank test versus the standard log-rank test.

Main Methods:

  • Derived the asymptotic distribution of the weighted log-rank test under local alternatives.
  • Developed sample size formulas for both optimal and standard log-rank tests.
  • Conducted simulation studies to assess formula adequacy and test efficiency.

Main Results:

  • Proposed sample size formulas provide adequate estimation for trial designs.
  • The optimal log-rank test demonstrates higher efficiency than the standard log-rank test.
  • This efficiency advantage is most pronounced with small cure rates and small censoring percentages.

Conclusions:

  • The developed sample size calculations are reliable for clinical trial planning.
  • The optimal log-rank test offers a more efficient approach for comparing cure rates, especially in challenging scenarios.