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来源:统计系  作者:统计系     日期:2017/11/9 22:17:10   点击数:389  

题目Improvement Screening for Ultra-High Dimensional Data with Censored      Survival Outcomes and Varying Coefficients

时间: 20171116日,星期四上午10:30 am

地点: X2511 


简介:岳博士于2008年获新加坡教育部全额奖学金赴新加坡南洋理工大学留学,2012年以一等荣誉学位(数学与经济双学位)毕业。同年获得留校任教资格。2014年获新加坡国立大学全额奖学金攻读统计学博士学位,师从栗家量教授,于201711月获得博士学位。现于新加坡国立大学公共卫生学院从事博士后工作,研究方向包括:survival analysis; ultra-high dimensional data analysis; variable screening; boosting; structure identification; infectious disease


Motivated by risk prediction studies with ultra-high dimensional bio markers, we propose a novel improvement screening methodology. Accurate risk prediction can be quite useful for patient treatment selection, prevention strategy or disease management in evidence-based medicine. The question of how to choose new markers in addition to the conventional ones is especially important. In the past decade, a number of new measures for quantifying the added value from the new markers were proposed, among which the integrated discrimination improvement (IDI) and net reclassification improvement (NRI) stand out. Meanwhile, C-statistics are routinely used to quantify the capacity of the estimated risk score in discriminating among subjects with different event times. In this paper, we will examine these improvement statistics as well as the norm-based approach for evaluating the incremental values of new markers and compare these four measures by analyzing ultra-high dimensional censored survival data. In particular, we consider Cox proportional hazards models with varying coefficients. All measures perform very well in simulations and we illustrate our methods in an application to a lung cancer study.