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Optimizing Sample Size Allocation and Power in a Bayesian Two-Stage Drop-The-Losers Design.

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Summary

This study introduces a user-friendly R Shiny tool to simplify planning Bayesian two-stage adaptive designs for clinical trials. The software helps researchers optimize patient allocation and estimate power, making complex trial designs more accessible.

Keywords:
R Shinyadaptive designspowerseamless phase II/III clinical trialstaged designtwo-stage trial

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Health Informatics

Background:

  • Traditional single-stage clinical trial designs can be inefficient when testing multiple treatments against a control.
  • Two-stage adaptive designs, such as seamless Phase II-III trials, offer increased efficiency but pose statistical planning and analysis challenges.

Purpose of the Study:

  • To develop a user-friendly graphical user interface (GUI) using R Shiny to facilitate the planning of Bayesian two-stage drop-the-losers adaptive designs.
  • To lower the barriers for researchers and statisticians in utilizing complex adaptive trial designs.

Main Methods:

  • Developed a point-and-click GUI in R Shiny for Bayesian two-stage adaptive designs.
  • The software allows users to obtain trial operating characteristics, estimate sample size and statistical power, and optimize patient allocation across stages.
  • Assumes normally distributed endpoints with common unknown variance across treatment arms.

Main Results:

  • The R Shiny application provides an accessible platform for researchers to plan and analyze two-stage adaptive designs.
  • Enables easy estimation of key design parameters like power and sample size.
  • Facilitates optimization of patient allocation strategies to maximize statistical power.

Conclusions:

  • The developed R Shiny software simplifies the implementation of Bayesian two-stage adaptive designs, promoting their wider adoption.
  • Encourages collaboration between statisticians and researchers in designing and evaluating adaptive trials.
  • Offers a valuable tool for investigating the power of two-stage designs compared to traditional methods.