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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Decision-making with multiple correlated binary outcomes in clinical trials.

Xynthia Kavelaars1, Joris Mulder1,2, Maurits Kaptein2

  • 1Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands.

Statistical Methods in Medical Research
|July 17, 2020
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This study introduces a new Bayesian statistical framework for analyzing multiple correlated outcomes in clinical trials. It enables better treatment superiority decisions by accounting for outcome relationships and allowing for compensatory effects.

Keywords:
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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Medical Decision Making

Background:

  • Clinical trials frequently assess multiple outcomes to determine treatment effectiveness.
  • Existing statistical methods often analyze outcomes independently and lack compensatory mechanisms for decision-making.
  • This can lead to suboptimal or inefficient conclusions regarding treatment superiority.

Purpose of the Study:

  • To propose a novel Bayesian framework for analyzing correlated binary outcomes in clinical trials.
  • To introduce a flexible decision criterion that incorporates a compensatory mechanism for outcome importance.
  • To improve the accuracy and efficiency of treatment superiority decisions in multidimensional outcome settings.

Main Methods:

  • Development of a Bayesian model utilizing the multivariate Bernoulli distribution for correlated binary outcomes.
  • Implementation of a flexible decision criterion with a compensatory mechanism to weigh the relative importance of different outcomes.
  • Conducting a simulation study to evaluate the proposed framework's performance across various trial designs and prior distributions.

Main Results:

  • The proposed Bayesian framework allows for efficient and unbiased superiority decisions in the presence of correlated outcomes.
  • Type I error rates are demonstrated to be properly controlled within the simulation study.
  • The framework shows robust performance across fixed, group sequential, and adaptive clinical trial designs.

Conclusions:

  • The novel Bayesian approach addresses limitations of traditional methods for multiple outcome analysis in clinical trials.
  • The flexible decision criterion enhances the ability to make nuanced and clinically relevant superiority judgments.
  • This framework offers a more comprehensive and compensatory approach to evaluating treatment effects across multiple endpoints.