Optimal, Two Stage, Adaptive Enrichment Designs for Randomized Trials, using Sparse Linear Programming
*Michael Rosenblum, Johns Hopkins Bloomberg School of Public Health Keywords: Adaptive enrichment designs involve preplanned rules for modifying enrollment criteria based on accruing data in a randomized trial. These designs can be useful when it is suspected that treatment effects may differ in certain subpopulations, such as those defined by a biomarker or risk factor at baseline. 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 strongly 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 tools to solve this problem to high accuracy, revealing new, optimal designs. This is joint work with Xingyuan (Ethan) Fang and Han Liu at Princeton University.
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Key Dates
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June 3, 2014 - September 7, 2015
Online Registration -
June 3, 2015 - August 15, 2015
Housing -
July 31 - August 17, 2015
Invited Abstract Editing -
August 10, 2015
Short Course materials due from Instructors -
August 26, 2015
Advanced Registration Deadline -
September 7, 2015
Cancellation Deadline -
September 16 - 18, 2015
Marriott Wardman Park, Washington, DC