A semiparametric empirical likelihood method for biased sampling schemes with auxiliary covariates
A semiparametric empirical likelihood method for biased sampling schemes with auxiliary covariates
复制标题
DOI：
10.1111/j.1541-0420.2006.00612.x
复制
发表时间：
2006-12-01
期刊：
影响因子：
1.9
通讯作者：
Zhou, Haibo
中科院分区：
文献类型：
--
作者：
Wang, Xiaofei;Zhou, Haibo
我们考虑对来自流行病学研究的数据进行半参数推理程序，该研究采用两部分抽样方案进行，其中观察到简单随机样本和多个结局/辅助依赖样本。这种抽样方案允许研究人员对某些被认为拥有有关回归模型更多信息的子群体进行过度抽样，同时仍然通过简单的随机样本获得有关潜在群体的见解。我们关注的是没有关于父队列的额外信息并且采样概率不可识别的设置。我们通过一项正在进行的研究来激发我们的问题，该研究旨在评估非小细胞肺癌患者表皮生长因子受体 (EGFR) 突变水平与 EGFR 靶向治疗的抗肿瘤反应之间的关联。所提出的方法适用于二元和多类别结果数据，并允许广义线性模型框架中的任意链接函数。仿真研究表明，所提出的估计量具有良好的小样本特性。通过数据示例说明了所提出的方法。
We consider a semiparametric inference procedure for data from epidemiologic studies conducted with a two-component sampling scheme where both a simple random sample and multiple outcomeor outcome-/auxiliary-dependent samples are observed. This sampling scheme allows the investigators to oversample certain subpopulations believed to have more information about the regression model while still gaining insights about the underlying population through the simple random sample. We focus on settings where there is no additional information about the parent cohort and the sampling probability is nonidentifiable. We motivate our problem with an ongoing study to assess the association between the mutation level of epidermal growth factor receptor (EGFR) and the antitumor response to EGFR-targeted therapy among nonsmall cell lung cancer patients. The proposed method applies to both binary and multicategorical outcome data and allows an arbitrary link function in the framework of generalized linear models. Simulation studies show that the proposed estimator has nice small sample properties. The proposed method is illustrated with a data example.