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Optimizing Randomized Trial Designs to Distinguish which Subpopulations Benefit from Treatment
Biometrika (2011)
  • Michael Rosenblum, Johns Hopkins University
  • Mark J. van der Laan, University of California - Berkeley
It is a challenge to evaluate experimental treatments where it is suspected that the treatment effect may only be strong for certain subpopulations, such as those having a high initial severity of disease, or those having a particular gene variant. Standard randomized controlled trials can have low power in such situations. They also are not optimized to distinguish which subpopulations benefit from a treatment. With the goal of overcoming these limitations, we consider randomized trial designs in which the criteria for patient enrollment may be changed, in a preplanned manner, based on interim analyses. Since such designs allow data-dependent changes to the population enrolled, care must be taken to ensure strong control of the familywise Type I error rate. Our main contribution is a general method for constructing randomized trial designs that allow changes to the population enrolled based on interim data using a prespecified decision rule, make no parametric model assumptions, and guarantee the asymptotic, familywise Type I error rate is strongly controlled at a specified level. As a demonstration of our method, we prove new, sharp results for a simple, two stage enrichment design. We then compare this design to fixed designs, focusing on each design's ability to determine overall and subpopulation specific treatment effects.
  • adaptive clinical trial design,
  • enrichment
Publication Date
December, 2011
Citation Information
Michael Rosenblum and Mark J. van der Laan. "Optimizing Randomized Trial Designs to Distinguish which Subpopulations Benefit from Treatment" Biometrika Vol. 98 Iss. 4 (2011)
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