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[Comparison of Bayesian interim analysis and classical interim analysis in group sequential design].

Lingling Yuan1, Zhiying Zhan, Xuhui Tan

  • 1School of Humanities and Management, Southern Medical University, Guangzhou 510515, China.E-mail: txhyll@163.com.

Nan Fang Yi Ke Da Xue Xue Bao = Journal of Southern Medical University
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Bayesian interim analysis offers controlled Type I error with skeptical or handicap priors. However, these methods may reduce power compared to classical designs, impacting clinical trial efficiency.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design

Background:

  • Interim analysis allows early trial termination based on accumulating data.
  • Classical and Bayesian approaches to interim analysis have distinct methodologies and implications.

Purpose of the Study:

  • To compare Bayesian and classical interim analysis methods.
  • To evaluate Type I error, power, sample size, and stage efficiency under different prior distributions.

Main Methods:

  • A superior hypothesis test compared means of two independent samples (control vs. treatment).
  • Bayesian interim analysis evaluated Type I error, power, average sample size, and average stage using various prior distributions.
  • Group sequential design data requirements were considered.

Main Results:

  • Bayesian interim analysis with skeptical and handicap priors controlled Type I error around 0.05.
  • Bayesian methods with these priors showed lower power than classical designs at 80% power.
  • Non-informative and enthusiastic priors resulted in powers distinctly higher than 80%.

Conclusions:

  • Bayesian interim analysis with skeptical and handicap priors effectively controls Type I error.
  • These Bayesian approaches can increase early clinical trial completion rates compared to O'Brien & Fleming.
  • Bayesian methods with skeptical/handicap priors lack practical value for Pocock group sequential designs.