报告题目：Instrumental Variable Estimation of Complier Causal Treatment Effect with Interval Censored Data
报 告 人：李树威 副教授 广州大学
报告时间：2020年6月20日上午9:00—10:00
报告地点：腾讯会议 会议ID：132 584 737
会议密码：200620
或链接：https://meeting.tencent.com/s/MeRxjXTwfbLR
校内联系人：王培洁 wangpeijie@jlu.edu.cn
报告摘要：
Assessing causal treatment effect on a time-to-event outcome is of key interest in many scientific investigations. Instrumental variable (IV) is a useful tool to mitigate the impact of endogenous treatment selection to attain unbiased estimation of causal treatment effect. Existing development of IV methodology, however, hasn’t attended to outcomes subject to interval censoring, which are ubiquitously present in studies with intermittent follow-up but are challenging to handle in terms of both theory and computation. In this work, we fill in this important gap by studying general causal semiparametric linear transformation models with interval-censored data. We propose a non-parametric maximum likelihood estimator of the complier causal treatment effect. Moreover, we design a reliable and computationally stable EM algorithm which has a tractable objective function in the maximization step via the use of Poisson latent variables. The asymptotic properties of the proposed estimators, including the consistency, asymptotic normality, and semiparametric efficiency, are established with empirical process techniques. We conduct extensive simulation studies and an application to a colorectal cancer screening dataset, showing satisfactory finite-sample performance of the proposed method as well as its prominent advantages over naive methods.
报告人简介：
李树威，广州大学统计系副教授、研究生导师。研究领域为生物统计、生存分析、纵向数据等。目前已发表多篇SCI论文。
