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Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

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Published on: October 23, 2020

On sample size calculation for comparing survival curves under general hypothesis testing.

Sin-Ho Jung1, Shein-Chung Chow

  • 1Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA. jung0005@mc.duke.edu

Journal of Biopharmaceutical Statistics
|March 16, 2012
PubMed
Summary

This study extends the modified log-rank test for diverse clinical trial objectives, offering flexible sample size calculations. The proposed methods demonstrate reliable performance, even with small sample sizes.

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Survival Analysis

Background:

  • The log-rank test is standard for comparing survival distributions with right censoring.
  • Jung et al. (2005) introduced a modified log-rank test for noninferiority trials and sample size calculations.
  • Existing methods lack flexibility for nonconventional study objectives.

Purpose of the Study:

  • To extend the modified log-rank test for clinical trials with various nonconventional objectives.
  • To propose a flexible sample size calculation method for these extended applications.
  • To provide a robust tool for designing clinical trials with general null and alternative hypotheses.

Main Methods:

  • Extension of the modified log-rank test for broader clinical trial applications.
  • Development of a flexible sample size calculation formula accommodating diverse survival distributions and accrual patterns.
  • Validation through simulations and illustration with real clinical trial designs.

Main Results:

  • The extended modified log-rank test accommodates various nonconventional study objectives.
  • The proposed sample size formula offers flexibility for specifying survival distributions and accrual patterns.
  • Simulations confirm satisfactory performance of the test and sample size formula, even in small samples.

Conclusions:

  • The modified log-rank test and proposed sample size calculation are valuable for designing complex clinical trials.
  • The methods provide a flexible and reliable approach for survival analysis in clinical research.
  • This work enhances the statistical toolkit for clinical trial design and analysis.