报告题目：Cox Regression with Nonignorable Survival-Dependent

报告人： Professor Jun Shao

Department of Statistics, University of Wisconsin-Madison

http://www.stat.wisc.edu/~shao/

时间地点：2017年12月6日(星期三)下午3:00-4:00

浙江大学玉泉校区工商管理楼一楼报告厅（105教室）

摘要：Analysis with censored survival time in clinical and epidemiological studies often encounters missing covariate data and a missing at random assumption is commonly adopted, which assumes that missingness depends on observed censored data, the minimum of survival and censoring time. Although missingness is likely related with time of survival, sometimes it is not reasonable to assume that censoring affects missingness of a covariate, especially when covariates are measured at baseline. If missingness of a covariate depends on survival time (and other covariates with no missing values), but not censoring, then missingness is nonignorable since survival time may be censored, and data analysis is challenging. In this article we propose a method in Cox regression with survival-dependent missing covariates, which is shown to produce consistent and asymptotically normal estimators of parameters. Our method is based on inverse propensity weighting with both censored and non-censored survivals. The propensity depending on non-censored survival is estimated nonparametrically by product kernel regression. The finite-sample performance of the proposed estimators is examined through simulation and by an application to a real-data example.

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联系人: 张立新教授 stazlx@zju.edu.cn

邵军教授简介：美国威斯康辛-麦迪逊大学统计系教授。1982年本科毕业于华东师范大学数学系，1983年赴美国求学深造，1987年取得威斯康星大学麦迪逊分校统计学博士学位，1996年起在斯康星大学麦迪逊分校担任统计学教授至今。邵军教授曾于1996年当选国际数理统计学会院士，于1999年当选美国统计学会院士，现为华东师范大学特聘教授。曾任国际泛华统计学会主席，JASA(Journal of American Statistical Association)、Statistica Sinica副主编，Journal of Multivariate Analysis和Sankhya联合主编，现任Journal of Nonparametric Statistics主编，Journal of System Science and Complexity联合主编。邵军教授主要研究方向是刀切法、自助法等重抽样，高维数据变量选择，缺失数据，纵向数据等，自1987年以来已经发表170多篇学术论文，由他编纂的《数理统计（Mathematical Statistics）》已经在数理统计领域成为名著，并被北美和中国多所大学选为统计学研究生教材。