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Power and sample size calculation for paired recurrent events data based on robust nonparametric tests.

Pei-Fang Su1, Chia-Hua Chung1, Yu-Wen Wang2

  • 1Department of Statistics, National Cheng Kung University, Tainan, 70101, Taiwan.

Statistics in Medicine
|February 10, 2017
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Summary

This study introduces a new sample size formula for paired recurrent events data, enhancing statistical power in clinical research. The formula uses non-parametric tests and accounts for complex event correlations, improving study design for recurrent event analysis.

Keywords:
correlated gamma frailtycumulative mean functionmixed Poisson processnon-parametric test

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Recurrent events are common in medical studies, posing challenges for sample size calculation.
  • Existing methods may not adequately address the complexities of paired recurrent event data and their correlations.

Purpose of the Study:

  • To develop a robust formula for determining the necessary sample size for paired recurrent events data.
  • To provide a method that accommodates associations within sequences of paired event times.

Main Methods:

  • Developed a sample size formula based on robust non-parametric tests for marginal mean function comparison.
  • Utilized correlated gamma frailty variables within a proportional intensity model to handle event associations.
  • Conducted comprehensive simulations to evaluate the formula's performance under various conditions.

Main Results:

  • The proposed formula effectively calculates sample sizes for paired recurrent events data.
  • Simulations demonstrated the method's robustness across different correlation structures, event processes, and censoring rates.
  • The formula's practical application was illustrated using a real-world premature infant study.

Conclusions:

  • The developed sample size formula offers a valuable tool for researchers analyzing paired recurrent events.
  • Accurate sample size determination is crucial for the validity and power of studies involving recurrent events.
  • This method enhances the design and analysis of clinical trials with paired recurrent event outcomes.