The identification of complex interactions in epidemiology and toxicology: a simulation study of boosted regression trees.
The identification of complex interactions in epidemiology and toxicology: a simulation study of boosted regression trees.
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流行病学和毒理学中复杂相互作用的鉴定：增强回归树的模拟研究。
DOI：
10.1186/1476-069x-13-57
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发表时间：
2014-07-04
期刊：
影响因子：
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通讯作者：
Bornefalk-Hermansson A
中科院分区：
文献类型：
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作者：
Lampa E;Lind L;Lind PM;Bornefalk-Hermansson A
有必要评估复杂的相互作用对人类健康的影响，例如环境污染物混合物引起的影响。通常的方法是制定一个可加性统计模型，并使用感兴趣的变量之间的乘积项检查偏离。在本文中，我们提出了一种方法来搜索多个变量之间的交互作用，使用提升回归树。我们模拟连续的结果，从真实的数据27个环境污染物，其中一些是相关的，并测试该方法的能力，发现模拟的相互作用。模拟结果包含一个四向交互作用，一个非线性效应和一个连续变量和二进制变量之间的交互作用。四个场景反映了不同的关联强度进行了模拟。我们使用真实的数据来说明该方法。该方法成功地确定了真正的相互作用，在所有的情况下，除了关联最弱的地方。然而，也发现了一些虚假的相互作用。该方法也能够识别在真实的数据集的相互作用。我们的结论是，增强回归树可以用来揭示复杂的相互作用的影响，在流行病学研究。
There is a need to evaluate complex interaction effects on human health, such as those induced by mixtures of environmental contaminants. The usual approach is to formulate an additive statistical model and check for departures using product terms between the variables of interest. In this paper, we present an approach to search for interaction effects among several variables using boosted regression trees. We simulate a continuous outcome from real data on 27 environmental contaminants, some of which are correlated, and test the method’s ability to uncover the simulated interactions. The simulated outcome contains one four-way interaction, one non-linear effect and one interaction between a continuous variable and a binary variable. Four scenarios reflecting different strengths of association are simulated. We illustrate the method using real data. The method succeeded in identifying the true interactions in all scenarios except where the association was weakest. Some spurious interactions were also found, however. The method was also capable to identify interactions in the real data set. We conclude that boosted regression trees can be used to uncover complex interaction effects in epidemiological studies.