# Canadian Journal of Statistics

## Response adaptive designs with asymptotic optimality

### Abstract

This article discusses the asymptotic optimality of statistical inference for response‐adaptive designs, which has ethical advantages over traditional methods for clinical trials. The upper bound of statistical power of asymptotically level α tests is derived and the Wald statistic is shown to be asymptotically optimal in terms of achieving the upper bound of the asymptotic power. The rates of coverage error probability of the confidence interval are proven to depend on the convergence rate of the allocation proportions for non‐normally distributed responses. When the response density functions are normal density functions, it is proven that the coverage error probability and type I error rate are of the order ${n}^{-1}$. The Canadian Journal of Statistics 46: 458–469; 2018 © 2018 Statistical Society of Canada

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