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Modified power prior with multiple historical trials for binary endpoints.

Akalu Banbeta1,2, Joost van Rosmalen3, David Dejardin4

  • 1I-Biostat, UHasselt, Hasselt, Belgium.

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|October 26, 2018
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Summary
This summary is machine-generated.

Incorporating historical data into clinical trials using Bayesian methods, specifically the modified power prior (MPP), can enhance statistical power and reduce sample sizes. This study adapts MPP for multiple historical control arms, improving trial efficiency.

Keywords:
Bayesian inferencedependent weightsmodified power priormultiple historical trials

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacometrics

Background:

  • Historical data can improve clinical trial power and reduce sample size requirements.
  • Bayesian methods, particularly power priors, offer a framework for incorporating historical data.
  • The modified power prior (MPP) is an extension for estimating the weight parameter from data, typically used for single historical trials.

Purpose of the Study:

  • To adapt the modified power prior (MPP) for incorporating multiple historical control arms into current clinical trial analyses.
  • To investigate the performance of different priors for weight parameters (independent, dependent, robustified dependent) in the context of multiple historical controls.
  • To compare the proposed Bayesian methods against existing approaches for utilizing historical control data.

Main Methods:

  • Adaptation of the modified power prior (MPP) to handle multiple historical control arms, each with a distinct weight parameter.
  • Implementation and evaluation of three distinct priors for the weight parameters: independent, dependent, and robustified dependent.
  • Analysis of two real-life clinical trial datasets and extensive simulation studies to assess method performance.

Main Results:

  • The dependent power prior demonstrates improved statistical power by effectively borrowing information from comparable historical studies.
  • The robustified dependent power prior offers protection against potential conflicts between historical and current trial data.
  • Bayesian methods incorporating historical control arms can enhance the efficiency and power of current clinical trial analyses.

Conclusions:

  • The adapted MPP framework effectively incorporates multiple historical control arms, offering a flexible approach for Bayesian clinical trial design.
  • Dependent and robustified dependent priors provide valuable options for leveraging historical data while managing potential data conflicts.
  • These methods hold promise for optimizing clinical trial resource allocation and improving the reliability of study findings.