Stochastic optimization of adaptive enrichment designs for two subpopulations.

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.

journal_name

J Biopharm Stat

authors

Fisher A,Rosenblum M,Alzheimer’s Disease Neuroimaging Initiative.

doi

10.1080/10543406.2018.1489401

subject

Has Abstract

pub_date

2018-01-01 00:00:00

pages

966-982

issue

5

eissn

1054-3406

issn

1520-5711

journal_volume

28

pub_type

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