报告题目：Some Recent Developments on Exact Inferences Using Binary Data in Two or Multi-stage Designs

报告时间：2017年6月16日 16:00

报告地点：统计学院办公楼1层104室

主讲人简介： Weizhen Wang（王维真） received his B.S. and M.S. at Peking University in 1987 and 1990, respectively, and completed his Ph.D. in Statistics at Cornell University in 1995. After one-year visit at Purdue University, he joined Wright State University, and has been a Professor of Statistics since 2007. His research includes bioequivalence, exact parametric and nonparametric inference, saturated and adaptive designs, categorical data analysis, foundation of statistics, statistical computation, dose-response study and causal inference. His current primary interest is exact statistical inference and its implementation in R

报告摘要：When establish an effective treatment using binary data from a two-stage design, one-sided tests for a proportion p are employed. Researchers typically use the parameter configuration at the boundary of the null hypothesis space to determine a rejection region. However, it is unclear whether the resultant test is of level, especially in adaptive designs. In this talk, we prove that this is true for a large family of tests in both nonadaptive and adaptive two-stage designs by showing that the power function of any test in the family is a nondecreasing function in p, then establish similar results for nonadaptive multi-stage designs with m(> 2) stages. In addition, we derive optimal lower one-sided 1-ａconfidence intervals for p. If time permits, we discuss exact tests for the bivariate case where both the response and the toxicity are considered.