報告題目：Instrumental Variable Estimation of Complier Causal Treatment Effect with Interval Censored Data
報 告 人：李樹威 副教授 廣州大學
報告時間：2020年6月20日上午9:00—10:00
報告地點：騰訊會議 會議ID：132 584 737
會議密碼：200620
或鍊接：https://meeting.tencent.com/s/MeRxjXTwfbLR
校内聯系人：王培潔 wangpeijie@jlu.edu.cn
報告摘要：
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.
報告人簡介：
李樹威，廣州大學統計系副教授、研究生導師。研究領域為生物統計、生存分析、縱向數據等。目前已發表多篇SCI論文。
