应学院邀请，河南理工大学刘中强教授将在线作学术报告。
报告题目：Subgroup adaptive randomization and model-based adaptive randomization for heteroscedasticity of treatment responses
报告摘要：A well-known issue when testing for treatment-by-subgroup interaction is its low power, as clinical trials are generally powered for establishing efficacy claims for the overall population, and they are usually not adequately powered for detecting interaction. Hence, it is necessary to develop an adaptive design to improve the efficiency of detecting heterogeneous treatment effects within subgroups. Considering Neyman allocation can maximize the power of usual 𝑍-test (see p. 194 of the book edited by Rosenberger and Lachin), we propose a subgroup-adaptive randomization procedure aiming to achieve Neyman allocation in both predefined subgroups and overall study population. To improve the power of interaction tests, we develop a model-based adaptive randomization procedures for heteroscedasticity of treatment responses, and derive its limiting allocation proportion, which is a generalization of the Neyman allocation. Simulation studies show that compared with complete randomization, the model-based randomization procedure has greater power to detect differences in systematic effects, main treatment effects and covariate effects.
报告时间：2022年11月13日16:00
报告地点：腾讯会议号(371267370)
邀 请 人：田玉柱 副教授
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报告人简介
刘中强，中国人民大学统计学博士，浙江大学生物统计方向博士后，硕士生导师。主持国家自然科学基金青年基金项目一项，主持中国博士后科学基金面上项目一项；参与国家自然科学基金面上项目、教育部人文社会科学统计学项目和国家统计局项目。代表性论文发表在Science China Mathematics, Biometrical Journal, Pharmaceutical Statistics, Journal of Statistical Planning and Inference等国际知名杂志上。
