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Bayesian MCPMod.

Frank Fleischer1, Sebastian Bossert1, Qiqi Deng2

  • 1Department of Biostatistics and Data Science, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany.

Pharmaceutical Statistics
|January 21, 2022
PubMed
Summary
This summary is machine-generated.

Bayesian MCPMod (BMCPMod) enhances dose-finding by systematically integrating historical data, overcoming frequentist limitations. This Bayesian approach mimics traditional methods while enabling robust historical data incorporation for improved trial design.

Keywords:
Bayesian borrowingMCPModcontrast testdose-findingdose-responsemultiple testing

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Area of Science:

  • Biostatistics
  • Pharmacometrics
  • Clinical Trial Design

Background:

  • Multiple Comparison Procedures and Modeling (MCPMod) is a standard frequentist method for dose-finding under model uncertainty.
  • The frequentist nature of MCPMod hinders systematic incorporation of historical data, such as placebo group data from previous trials.

Purpose of the Study:

  • To define Bayesian MCPMod (BMCPMod), a novel approach combining MCPMod principles with Bayesian methods for historical data integration.
  • To derive Bayesian characteristics analogous to MCPMod's testing component and compare BMCPMod with classical MCPMod.

Main Methods:

  • Developed Bayesian MCPMod (BMCPMod) by integrating meta-analytic prior concepts for systematic historical data inclusion.
  • Derived Bayesian testing characteristics for BMCPMod and conducted simulations to compare it with classical MCPMod using non-informative priors.

Main Results:

  • BMCPMod effectively mimics classical MCPMod results when using non-informative priors.
  • The simulations demonstrated the capability of BMCPMod to systematically incorporate historical data, offering a flexible alternative to frequentist methods.

Conclusions:

  • BMCPMod provides a robust Bayesian framework for dose-finding that overcomes the limitations of frequentist MCPMod regarding historical data integration.
  • The study discusses aspects like mixture priors, optimal contrast vectors, and allocation ratios, offering guidance for designing BMCPMod trials.