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ADAPTIVE ENRICHMENT DESIGNS FOR RANDOMIZED TRIALS WITH DELAYED ENDPOINTS, USING LOCALLY EFFICIENT ESTIMATORS TO IMPROVE PRECISION
Johns Hopkins University, Dept. of Biostatistics Working Papers
  • Michael Rosenblum, Johns Hopkins University, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
  • Tianchen Qian, Johns Hopkins University, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
  • Yu Du, Johns Hopkins University, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
  • Huitong Qiu, Johns Hopkins University, Johns Hopkins Bloomberg School of Public Health, Depaetment of Biostatistics
Date of this Version
4-24-2015
Abstract

Adaptive enrichment designs involve preplanned rules for modifying enrollment criteria based on accrued data in an ongoing trial. For example, enrollment of a subpopulation where there is sufficient evidence of treatment efficacy, futility, or harm could be stopped, while enrollment for the remaining subpopulations is continued. Most existing methods for constructing adaptive enrichment designs are limited to situations where patient outcomes are observed soon after enrollment. This is a major barrier to the use of such designs in practice, since for many diseases the outcome of most clinical importance does not occur shortly after enrollment. We propose a new class of adaptive enrichment designs for delayed endpoints. At each analysis, semiparametric, locally efficient estimators leverage information in baseline variables and short-term outcomes to improve precision. This can reduce the sample size required to achieve a desired power. We propose new multiple testing procedures tailored to this problem, which we prove to strongly control the family-wise Type I error rate, asymptotically. These methods are illustrated through simulations of a trial for a new surgical intervention for stroke.

Citation Information
Michael Rosenblum, Tianchen Qian, Yu Du and Huitong Qiu. "ADAPTIVE ENRICHMENT DESIGNS FOR RANDOMIZED TRIALS WITH DELAYED ENDPOINTS, USING LOCALLY EFFICIENT ESTIMATORS TO IMPROVE PRECISION" (2015)
Available at: http://works.bepress.com/michael_rosenblum/8/