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Dong Cailin1; Zhou Jie2; Sun Liuquan2
Source Publicationactamathematicaeapplicataesinica
AbstractCase-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.
Funding Project[National Natural Science Foundation of China] ; [Key Laboratory of RCSDS, CAS] ; [BCMIIS]
Document Type期刊论文
Affiliation1.School of Mathematics and Statistics, Huazhong Normal University
Recommended Citation
GB/T 7714
Dong Cailin,Zhou Jie,Sun Liuquan. aclassofweightedestimatorsforadditivehazardsmodelincasecohortstudies[J]. actamathematicaeapplicataesinica,2014,30(4):1153.
APA Dong Cailin,Zhou Jie,&Sun Liuquan.(2014).aclassofweightedestimatorsforadditivehazardsmodelincasecohortstudies.actamathematicaeapplicataesinica,30(4),1153.
MLA Dong Cailin,et al."aclassofweightedestimatorsforadditivehazardsmodelincasecohortstudies".actamathematicaeapplicataesinica 30.4(2014):1153.
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