Bootstrap methods for adaptive designs

Abstract

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-α 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.