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A subgroup cluster-based Bayesian adaptive design for precision medicine.

Wentian Guo1, Yuan Ji2,3, Daniel V T Catenacci4,5

  • 1Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China.

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|October 25, 2016
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Summary
This summary is machine-generated.

This study introduces SCUBA, a Bayesian adaptive design for precision medicine clinical trials. SCUBA identifies patient subgroups and assigns targeted therapies, optimizing treatment allocation and reporting subgroup-treatment pairs for future studies.

Keywords:
Adaptive designBayesian nonparametricsDirichlet processEnrichment designsPersonalized therapyReversible jump MCMC

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Precision Medicine

Background:

  • Precision medicine relies on targeted therapies predicted by patient characteristics.
  • Identifying unknown patient subgroups is crucial for effective treatment.
  • Current methods often lack efficient subgroup discovery within trials.

Purpose of the Study:

  • To present SCUBA (Subgroup Cluster-based Bayesian Adaptive design) for clinical trials.
  • To simultaneously enrich patient subgroups with effective treatments and identify subgroup-treatment pairs (STPs).
  • To enable probabilistic assessment of patient subgroups and their targeted therapies.

Main Methods:

  • Utilizes random partitions and semiparametric Bayesian models.
  • Implements an adaptive design to dynamically allocate treatments.
  • Focuses on inferring patient subgroups from observed clinical data.

Main Results:

  • SCUBA effectively enriches treatment allocation for identified patient subgroups.
  • The design successfully reports multiple subgroup-treatment pairs (STPs).
  • Demonstrated application in a gastroesophageal cancer clinical trial through simulations.

Conclusions:

  • SCUBA offers a coherent and probabilistic approach to subgroup discovery in clinical trials.
  • Identified STPs can inform future confirmatory studies and regulatory approval.
  • The method enhances the efficiency and precision of adaptive clinical trial designs.