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A practical guide to pre-trial simulations for Bayesian adaptive trials using SAS and BUGS.

Christian Holm Hansen1, Pamela Warner2, Allan Walker2,3

  • 1MRC Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK.

Pharmaceutical Statistics
|September 15, 2018
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Summary

Simulation studies are crucial for efficient adaptive trial designs. This guide offers a practical, step-by-step method using SAS and Bayesian models for feasible clinical trial simulations.

Keywords:
Bayesian modellingOpenBUGSSASWinBUGSadaptive trialssimulations

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Programming

Background:

  • Adaptive trial designs offer flexibility but require careful planning.
  • Simulation studies are essential for evaluating adaptive trial designs.
  • Implementing these simulations presents significant statistical programming challenges.

Purpose of the Study:

  • To provide a practical, step-by-step guide for conducting pre-trial simulation studies.
  • To demonstrate how to integrate Bayesian modeling (WinBUGS/OpenBUGS) with SAS for adaptive trial simulations.
  • To offer guidance on using simulation results for adapting allocation probabilities in subsequent trial stages.

Main Methods:

  • Utilizing SAS for defining and controlling the entire simulation workflow.
  • Specifying and fitting Bayesian models within WinBUGS or OpenBUGS.
  • Integrating Bayesian analysis results back into SAS for adaptive design modifications.
  • Simulating subsequent stages of a clinical trial based on adaptive allocation probabilities.

Main Results:

  • A detailed, practical guide for implementing complex pre-trial simulation studies.
  • Demonstration of a seamless interface between SAS and Bayesian software (WinBUGS/OpenBUGS).
  • A method to control the entire simulation exercise within a single SAS program.

Conclusions:

  • The proposed approach simplifies the implementation of simulation studies for adaptive trial designs.
  • Integrating Bayesian analysis and SAS provides a powerful and efficient tool for trial design.
  • This methodology enhances the feasibility and statistical rigor of adaptive clinical trial planning.