Asymptotic Inference with Response-Adaptive Treatment Allocation Designs

Abstract

A response-adaptive treatment allocation design for a clinical trial attempts to place the majority of patients on the treatment that appears more successful, based on the responses of patients already treated. One example of such a design is the randomized play-the-winner rule developed by Wei and Durham, which randomizes the treatment assignment probabilities according to the outcomes of treatments previously assigned. For a trial with dichotomous treatment responses and a randomized play-the-winner assignment scheme, exact small sample permutation tests of the hypothesis of equal treatment effects and large sample tests based on a population model have previously been developed. We present a large sample permutation test statistic for this case; under certain conditions on the sequence of responses, the test statistic is shown to be asymptotically normal. For a trial with a continuous response variable, we develop a rank-based analog of the randomized play-the-winner assignment scheme. Simulation evidence in both cases suggests that a normal approximation to the test statistic works well for moderate-sized trials, with some conservatism in the extreme tails.