Outcome-dependent sampling design and inference for Cox's proportional hazards Model

被引:9
作者
Yu, Jichang [1 ,2 ]
Liu, Yanyan [2 ]
Cai, Jianwen [3 ]
Sandler, Dale P. [4 ]
Zhou, Haibo [3 ]
机构
[1] Zhongnan Univ Econ & Law, Sch Math & Stat, Wuhan 430073, Hunan, Peoples R China
[2] Wuhan Univ, Sch Math & Stat, Wuhan 430072, Hunan, Peoples R China
[3] Univ N Carolina, Dept Biostat, Chapel Hill, NC 27599 USA
[4] NIEHS, Epidemiol Branch, Res Triangle Pk, NC 27709 USA
基金
美国国家科学基金会;
关键词
Empirical process; Optimal allocation; Outcome-dependent sampling; CZECH URANIUM MINERS; MAXIMUM-LIKELIHOOD-ESTIMATION; CASE-COHORT DESIGN; LOGISTIC-REGRESSION; MORTALITY; EXPOSURE; EVENTS; RISK;
D O I
10.1016/j.jspi.2016.05.001
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We propose a cost-effective outcome-dependent sampling design for the failure time data and develop an efficient inference procedure for data collected with this design. To account for the biased sampling scheme, we derive estimators from a weighted partial likelihood estimating equation. The proposed estimators for regression parameters are shown to be consistent and asymptotically normally distributed. A criteria that can be used to optimally implement the ODS design in practice is proposed and studied. The small sample performance of the proposed method is evaluated by simulation studies. The proposed design and inference procedure is shown to be statistically more powerful than existing alternative designs with the same sample sizes. We illustrate the proposed method with an existing real data from the Cancer Incidence and Mortality of Uranium Miners Study. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:24 / 36
页数:13
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