|Title||Optimal, Two Stage, Adaptive Enrichment Designs for Randomized Trials, using Sparse Linear Programming|
|Year of Publication||2014|
Adaptive enrichment designs involve preplanned rules for modifying enrollment criteria based on accruing data in a randomized trial. Such designs have been proposed when the population of interest consists of biomarker positive and biomarker negative participants. If the biomarker negative population shows no signal of benefit from treatment at an interim analysis, future enrollment may be restricted to the biomarker positive population.
Two critical components of adaptive enrichment designs are the decision rule for modifying enrollment, and the multiple testing procedure.
We provide a general method for simultaneously optimizing both of these components for two stage, adaptive enrichment designs. The optimality criteria are defined in terms of expected sample size and power, under the constraint that the familywise Type I error rate is controlled. It is infeasible to directly solve this optimization problem since it is not convex.
The key to our approach is a novel representation of a discretized version of this optimization problem as a sparse linear program. We apply advanced optimization methods to solve this problem to high accuracy, revealing new, optimal designs.
This is joint work with Xingyuan Fang and Han Liu.
Optimal, Two Stage, Adaptive Enrichment Designs for Randomized Trials, using Sparse Linear Programming