A partial linear model in the outcome-dependent sampling setting to evaluate the effect of prenatal PCB exposure on cognitive function in children.
A partial linear model in the outcome-dependent sampling setting to evaluate the effect of prenatal PCB exposure on cognitive function in children.
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DOI：
10.1111/j.1541-0420.2010.01500.x
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发表时间：
2011-09
期刊：
影响因子：
1.9
通讯作者：
Longnecker MP
中科院分区：
文献类型：
--
作者：
Zhou H;Qin G;Longnecker MP
结果依赖抽样（ODS）是一种有效提高研究效率的方法，在生物医学研究中得到了广泛的应用。然而，在连续结果的设置中，由于理论和计算方面的挑战，暴露变量的表示仅限于线性模型的框架。部分线性模型（PLM）是一个强大的推理工具，非参数模型的结果和暴露变量之间的关系。在这篇文章中，我们考虑一个案例研究的部分线性模型的数据从ODS设计。我们提出了一个半参数的最大似然方法进行推断与PLM。我们开发的渐近性质和进行模拟研究表明，建议ODS估计可以产生一个更有效的估计比传统的简单随机抽样设计相同的样本容量。使用这种新开发的方法，我们能够探索流行病学中的一个开放问题：子宫内暴露于背景水平的PCB是否与儿童的智力障碍有关。我们的模型为低水平PCB暴露与儿童认知功能之间的关系提供了进一步的见解。这些结果为一系列不一致的流行病学发现提供了新的线索。
Outcome-dependent sampling (ODS) has been widely used in biomedical studies because it is a cost effective way to improve study efficiency. However, in the setting of a continuous outcome, the representation of the exposure variable has been limited to the framework of linear models, due to the challenge in terms of both theory and computation. Partial linear models (PLM) are a powerful inference tool to nonparametrically model the relation between an outcome and the exposure variable. In this article, we consider a case study of a partial linear model for data from an ODS design. We propose a semiparametric maximum likelihood method to make inferences with a PLM. We develop the asymptotic properties and conduct simulation studies to show that the proposed ODS estimator can produce a more efficient estimate than that from a traditional simple random sampling design with the same sample size. Using this newly developed method, we were able to explore an open question in epidemiology: whether in utero exposure to background levels of PCBs is associated with children’s intellectual impairment. Our model provides further insights into the relation between low-level PCB exposure and children’s cognitive function. The results shed new light on a body of inconsistent epidemiologic findings.
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DOI：
10.1198/016214504000001853
发表时间：
2005-06-01
影响因子：
3.7
作者：
Weaver, MA;Zhou, HB
通讯作者：
Zhou, HB
影响因子：
5.6
作者：
Rogan, W J;Gladen, B C
通讯作者：
Gladen, B C
影响因子：
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作者：
Wang, Xiaofei;Zhou, Haibo
通讯作者：
Zhou, Haibo
影响因子：
1
作者：
Huang, Jianhua Z.;Zhang, Liangyue;Zhou, Lan
通讯作者：
Zhou, Lan
影响因子：
10.4
作者：
Schantz, Susan L;Widholm, John J;Rice, Deborah C
通讯作者：
Rice, Deborah C