学术报告：Sample size and power calculation for propensity score analysis of observational studies

报告时间：4月2日（星期三）上午10:00-11:00

报告地点：沙河校区，学院1号楼102会议室

主持人：杨玥含 教授

报告人：李凡，杜克大学，教授

报告摘要：Sample size and power calculations in causal inference with observational data are increasingly desired, but related tools were lacking. This paper develops theoretically justified analytical formulas for sample size and power calculation in the propensity score analysis of causal inference. By analyzing the variance of the inverse probability weighting estimator of the average treatment effect (ATE), we clarify the three key components for sample size calculations: propensity score distribution, potential outcome distribution, and their correlation. We devise analytical procedures to identify these components based on commonly available and interpretable summary statistics. We elucidate the critical role of covariate overlap between treatment groups in determining the sample size. In particular, we propose to use the Bhattacharyya coefficient as a measure of covariate overlap, which, together with the treatment proportion, leads to a uniquely identifiable and easily computable propensity score distribution. The proposed method is applicable to both continuous and binary outcomes. We show that the standard two-sample z-test and variance inflation factor methods often lead to, sometimes vastly, inaccurate sample size estimates, especially with limited overlap. We also derive formulas for the average treatment effects for the treated (ATT) and overlapped population (ATO) estimands. We provide simulated and real examples to illustrate the proposed method. We develop an associated R package PSpower. This is a joint work with Bo Liu and Xiaoxiao Zhou.

报告人简介：李凡是杜克大学统计科学系教授，在生物统计学与生物信息学系兼任教授。她的主要研究兴趣在于因果推断的统计方法和健康数据科学。她还从事贝叶斯分析和缺失数据的研究。她曾担任《应用统计学年鉴》中社会科学、生物统计学与政策板块的编辑，以及《JASA》和《Annals of Statistics》的副主编。她是美国统计协会（ASA）和数理统计学会（IMS）的当选会士。

撰稿人：刘洁

审稿人：邓露