数学与统计学院学术报告[2020] 059号

(高水平大学建设系列报告412号)

报告题目:  Instrumental Variable Estimation of Complier Causal Treatment Effect with Interval Censored Data

报告人：李树威（广州大学）

报告时间：2020年8月25日上午10:00-11:00　　　　　　　　　　　　　　　　

报告地点： 腾讯会议 会议号码：535 858 091　　 　　　　　　　　　　　　　　　　　

报告内容：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.

报告人简历: 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。

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数学与统计学院

2020年8月24日