报告题目：Analysis of Linear Transformation Models with Covariate Measurement Error and Interval Censoring
报告人：王所进（Suojin Wang, Texas A&M University）
时间：2019年6月4日（星期二）上午10：00—11：00
地点：苏州大学本部精正楼（数学楼）301
摘要： Among several semiparametric models, the Cox proportional hazard model is widely used to assess the association between covariates and the time-to-event when the observed time-to-event is interval-censored. Often covariates are measured with error. To handle this covariate uncertainty in the Cox proportional hazard model with the interval-censored data flexible approaches have been proposed. To fill a gap and broaden the scope of statistical applications to analyze time-to-event data with different models, a general approach is proposed for fitting the semiparametric linear transformation model to interval-censored data when a covariate is measured with error. The semiparametric linear transformation model is a broad class of models that includes the proportional hazard model and the proportional odds model as special cases. The proposed method relies on a set of estimating equations to estimate the regression parameters and the infinite-dimensional parameter. For handling interval censoring and covariate measurement error, a flexible imputation technique is used. Finite sample performance of the proposed method is judged via simulation studies. Finally, the suggested method is applied to analyze a real data set from an AIDS clinical trial.
报告人简介：
Dr. Suojin Wang is Associate Dean for Assessment in College of Science and a Professor of Statistics and Epidemiology & Biostatistics at Texas A&M University. He received his Ph.D. from the University of Texas at Austin. His research interests include semi- and non-parametric statistical methodology, missing and mis-measured data analyses, asymptotic theory, sample surveys, and applied statistics. He has over 160 peer-reviewed research publications. He was the Editor-in-Chief of Journal of Nonparametric Statistics during 2007-2012. He is an elected Fellow of the American Statistical Association, an elected Fellow of the Institute of Mathematical Statistics and an elected member of the International Statistical Institute. He received four major teaching awards from Texas A&M University, including the most prestigious University-level Distinguished Achievement Award in Teaching.
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