Inference on the Minimum Effective Dose Using Binary Data

This article proposes a confidence interval procedure for an open question posted by Tamhane and Dunnett regarding the inference on the minimum effective dose of a drug for binary data. We use a partitioning approach in conjunction with a confidence interval procedure to provide a solution to this problem. Binary data frequently arise in medical investigations in connection with dichotomous outcomes such as the development of a disease or the efficacy of a drug. The proposed procedure not only detects the minimum effective dose of the drug, but also provides estimation information on the treatment effect of the closest ineffective dose. Such information benefits follow-up investigations in clinical trials. We prove that, when the confidence interval for the pairwise comparison has (or asymptotically controls) confidence level 1 ? α, the stepwise procedure strongly controls (or asymptotically controls) the familywise error rate at level α, which is a key criterion in dose finding. The new method is compared with other procedures in terms of power performance and coverage probability using simulations. The simulated results shed new light on the discernible features of the new procedure. An example on the investigation of acetaminophen is included.