Response adaptive designs with asymptotic optimality

Yanqing Yi, Xuan Li

Research output: Contribution to journalArticlepeer-review


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

Original languageEnglish (US)
Pages (from-to)458-469
Number of pages12
JournalCanadian Journal of Statistics
Issue number3
StatePublished - Sep 2018
Externally publishedYes

Bibliographical note

Funding Information:
Yanqing Yi acknowledges research support from the Natural Sciences and Engineering Research Council of Canada (NSERC). Xuan Li acknowledges partial support from the Grant-in-Aid of Research, Artistry and Scholarship (GIA) awarded by the Office of the Vice President for Research, University of Minnesota.


  • Confidence interval
  • coverage error probability
  • most powerful test
  • order of type I error rate
  • response adaptive designs


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