應學院邀請，河南理工大學劉中強教授将在線作學術報告。
報告題目：Subgroup adaptive randomization and model-based adaptive randomization for heteroscedasticity of treatment responses
報告摘要：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.
報告時間：2022年11月13日16:00
報告地點：騰訊會議号(371267370)
邀 請 人：田玉柱 副教授
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報告人簡介
劉中強，中國人民大學統計學博士，浙江大學生物統計方向博士後，碩士生導師。主持國家自然科學基金青年基金項目一項，主持中國博士後科學基金面上項目一項；參與國家自然科學基金面上項目、教育部人文社會科學統計學項目和國家統計局項目。代表性論文發表在Science China Mathematics, Biometrical Journal, Pharmaceutical Statistics, Journal of Statistical Planning and Inference等國際知名雜志上。
