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.
Disciplines
Biostatistics | Statistical Methodology | Statistical Theory
Suggested Citation
Fisher, Aaron and Rosenblum, Michael, "STOCHASTIC OPTIMIZATION OF ADAPTIVE ENRICHMENT DESIGNS FOR TWO SUBPOPULATIONS" (December 2016). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 279.
https://biostats.bepress.com/jhubiostat/paper279
Media Format
flash_audio