Stochastic optimization of adaptive enrichment designs for two subpopulations

For the Alzheimer’s Disease Neuroimaging Initiative

Research output: Contribution to journalArticlepeer-review

Abstract

An adaptive enrichment design is a randomized trial that allows enrollment criteria to be modified at interim analyses, based on a preset decision rule. When there is prior uncertainty regarding treatment effect heterogeneity, these trial designs can provide improved power for detecting treatment effects in subpopulations. We present a simulated annealing approach to search over the space of decision rules and other parameters for an adaptive enrichment design. The goal is to minimize the expected number enrolled or expected duration, while preserving the appropriate power and Type I error rate. We also explore the benefits of parallel computation in the context of this goal. We find that optimized designs can be substantially more efficient than simpler designs using Pocock or O’Brien-Fleming boundaries.

Original languageEnglish (US)
Pages (from-to)966-982
Number of pages17
JournalJournal of biopharmaceutical statistics
Volume28
Issue number5
DOIs
StatePublished - Sep 3 2018

Keywords

  • Clinical trials
  • optimization
  • simulated annealing
  • treatment effect heterogeneity

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)

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