A Class of Weighted Estimators for Additive Hazards Model in Case-cohort Studies  

作　　者：Cai-lin DONG Jie ZHOU Liu-quan SUN 

机构地区：[1]School of Mathematics and Statistics,Huazhong Normal University [2]Academy of Mathematics and Systems Science,Chinese Academy of Sciences [3]School of Mathematics,Capital Normal University

出　　处：《Acta Mathematicae Applicatae Sinica》2014年第4期1153-1168,共16页应用数学学报（英文版）

基　　金：partly supported by the National Natural Science Foundation of China Grants(No.11231010,11171330 and 11101314);Key Laboratory of RCSDS,CAS(No.2008DP173182)and BCMIIS

摘　　要：Case-cohort sampling is a commonly used and efficient method for studying large cohorts. In many situations, some covariates are easily measured on all cohort subjects, and surrogate measurements of the expensive covariates also may be observed. In this paper, to make full use of the covariate data collected outside the case-cohort sample, we propose＇a class of weighted estimators with general time-varying weights for the additive hazards model, and the estimators are shown to be consistent and asymptotically normal. We also identify the estimator within this class that maximizes efficiency, and simulation studies show that the efficiency gains of the proposed estimator over the existing ones can be substantial in practical situations. A real example is provided.Case-cohort sampling is a commonly used and efficient method for studying large cohorts. In many situations, some covariates are easily measured on all cohort subjects, and surrogate measurements of the expensive covariates also may be observed. In this paper, to make full use of the covariate data collected outside the case-cohort sample, we propose＇a class of weighted estimators with general time-varying weights for the additive hazards model, and the estimators are shown to be consistent and asymptotically normal. We also identify the estimator within this class that maximizes efficiency, and simulation studies show that the efficiency gains of the proposed estimator over the existing ones can be substantial in practical situations. A real example is provided.

关 键 词：additive hazards case-cohort study stratified sampling survival data two-phase design 

分 类 号：O212.1[理学—概率论与数理统计]