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ASIED: a Bayesian adaptive subgroup-identification enrichment design.

Yanxun Xu1, Florica Constantine1, Yuan Yuan2

  • 1Department of Applied Mathematics and Statistics, Johns Hopkins University , Baltimore, Maryland, USA.

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|November 30, 2019
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Summary

This study introduces an Adaptive Subgroup-Identification Enrichment Design (ASIED) to find patient subgroups that benefit most from targeted therapies. ASIED dynamically adapts clinical trials to identify predictive biomarkers and optimize treatment strategies.

Keywords:
Adaptive enrichment designBayesian subgroup identificationbiomarkerdecision-makingmultilevel target product profile

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Genomic Medicine

Background:

  • Personalized medicine aims to tailor treatments using patient characteristics and biomarkers.
  • Identifying patient subgroups with differential treatment responses is crucial for targeted therapy development.
  • Adaptive trial designs are needed to dynamically adjust studies based on emerging data.

Purpose of the Study:

  • To propose a novel Adaptive Subgroup-Identification Enrichment Design (ASIED).
  • To simultaneously identify predictive biomarkers and patient subgroups with differential treatment effects.
  • To develop robust decision-making rules for adaptive population enrichment in clinical trials.

Main Methods:

  • Development of the ASIED framework incorporating adaptive subgroup identification and enrichment.
  • Construction of quantitative decision-making rules for interim analyses based on heterogeneous treatment effects.
  • Extensive simulations to evaluate the operating characteristics of ASIED.

Main Results:

  • The ASIED effectively identifies predictive biomarkers and responsive patient subgroups.
  • The design allows for dynamic adaptation of study entry criteria at interim analyses.
  • Simulations demonstrate desirable operating characteristics compared to alternative designs.

Conclusions:

  • ASIED offers a robust approach for developing targeted therapies by identifying patient subgroups.
  • The design facilitates adaptive enrichment strategies based on interim outcome data.
  • ASIED provides a valuable tool for optimizing clinical trial efficiency and treatment efficacy in personalized medicine.