时间：2019年12月16日（周一）10:00-11:00
地点：中北校区理科大楼A1716报告厅
题目：Efficient estimation of optimal regimes under a no direct effect assumption
报告人：刘林，Postdoctoral Fellow Biostatistics Department, Harvard University
摘要：
We derive new estimators of an optimal joint testing and treatment regime under the no direct effect (NDE) assumption that a given laboratory, diagnostic, or screening test has no effect on a patient’s clinical outcomes except through the effect of the test results on the choice of treatment. We model the optimal joint strategy using an optimal regime structural nested mean model (opt-SNMM). The proposed estimators are more efficient than previous estimators of the parameters of an opt-SNMM because they efficiently leverage the ‘no direct effect (NDE) of testing’ assumption. Our methods will be of importance to decision scientists who either perform cost-benefit analyses or are tasked with the estimation of the ‘value of information’ supplied by an expensive diagnostic test (such as an MRI to screen for lung cancer).
报告人简介：
刘林本科毕业于同济大学生物信息专业，于2018年获得哈佛大学生物统计学博士，从事生物数学，统计和因果推断相关的研究，现在在哈佛大学做博士后，从事半参数统计中高阶影响方程理论和最优动态规划治疗方面的研究。