Online Program Home
My Program

Abstract Details

Activity Number: 263
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #321254
Title: Optimizing Adaptive Enrichment Designs
Author(s): Aaron Fisher* and Michael Rosenblum
Companies: The Johns Hopkins University and Johns Hopkins Bloomberg School of Public Health
Keywords: Adaptive Trials ; Optimization ; Simulated Annealing ; Alpha Reallocation
Abstract:

Adaptive enrichment trial designs (AETDs) can be an attractive option when there is prior uncertainty in treatment heterogeneity across subpopulations. Unfortunately, such designs often require several tuning parameters. This makes it difficult to ascertain the value of adopting an AETD, or the relative benefits of one design versus another. Here we present a Simulated Annealing approach for optimizing the tuning parameters of an AETD, in order to find a best case implementation of a given AETD method in a given scientific application. Optimization is done with respect to either expected sample size, or expected trial duration, and subject to constraints on power. We use this optimization framework compare approximate best-case implementations of AETD methods based Type I error rate reallocation and on the covariance of the test statistics. We also compare against conventional choices for tuning parameters that approximate O'Brien Fleming boundaries and Pocock boundaries. We find empirical evidence that optimized designs can be substantially more efficient than either standard Pocock or O'Brien Fleming boundaries.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

 
 
Copyright © American Statistical Association