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Bayesian multiple testing for two-sample multivariate endpoints.

Mithat Gönen1, Peter H Westfall, Wesley Johnson

  • 1Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA. gonenm@mskcc.org

Biometrics
|May 24, 2003
PubMed
Summary
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This study introduces a new Bayesian method for analyzing multiple correlated variables in clinical trials. It helps determine if treatments have a real effect, even with complex data relationships.

Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Statistical Inference

Background:

  • Clinical studies frequently involve multiple variables with correlated outcomes and hypotheses.
  • Simultaneous testing is crucial for analyzing complex datasets in research.

Purpose of the Study:

  • To propose a novel multivariate mixture prior for treatment effects in clinical studies.
  • To develop a Bayesian multiple testing procedure for multivariate two-sample situations with unknown covariance.

Main Methods:

  • Developed a Bayesian multiple testing procedure.
  • Incorporated a multivariate mixture prior allowing for zero effects and correlations.
  • Addressed unknown covariance structures in multivariate data.

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Main Results:

  • Obtained posterior probabilities of no difference between treatment regimens for specific variables.
  • The proposed method accounts for correlations in effect sizes, outcomes, and test statistics.

Conclusions:

  • The developed Bayesian approach provides a robust framework for multiple testing in complex clinical trials.
  • The method facilitates accurate assessment of treatment effects in multivariate settings.