Bootstrap methods for adaptive designs.
Adaptive designs generate dependent sequences of random variables that are not exchangeable. Therefore, it is not obvious how to employ a resampling scheme for confidence interval estimation. We propose a simple procedure where observed response rates from an adaptive experiment are input to a simulation program. The program then generates sequences from the adaptive sampling scheme. We compare, via simulation, three bootstrap confidence intervals with the asymptotic confidence interval for two adaptive designs useful for clinical trials. A simple ranking of simulated response rates yields a confidence interval approximation with coverage close to 1-alpha in most cases. The method allows us to incorporate such complexities as staggered entry and delayed response. We give an example of its utility on a clinical trial of fluoxetine in depression.
Department of Mathematics and Statistics, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, Maryland 21250, USA.
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Rosenberger
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Hu
D018687:Antidepressive Agents, Second-Generation / Q000627:therapeutic use*; D017326:Clinical Trials, Phase III as Topic / Q000379:methods / Q000706:statistics & numerical data*; D003198:Computer Simulation; D016001:Confidence Intervals; D003863:Depression / Q000188:drug therapy*; D005473:Fluoxetine / Q000627:therapeutic use*; D006801:Humans; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*
D018687:Antidepressive Agents, Second-Generation; D005473:Fluoxetine