Decision trees in epidemiological research.
Decision trees in epidemiological research.
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DOI：
10.1186/s12982-017-0064-4
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发表时间：
2017
影响因子：
2.3
通讯作者：
French S
中科院分区：
文献类型：
--
作者：
Venkatasubramaniam A;Wolfson J;Mitchell N;Barnes T;JaKa M;French S
在许多研究中，确定在结果方面相对均匀的人口亚群是有意义的。这些亚组的性质可以提供对效果机制的洞察，并为量身定制的干预措施提出目标。然而，用标准的统计方法确定相关的子组可能具有挑战性。我们回顾了关于决策树的文献，决策树是一种基于协变量将人口划分为具有相似结果变量值的不同子组的技术。我们比较了两种决策树方法，即流行的分类与回归树（CART）技术和较新的条件推理树（CTree）技术，在模拟研究中评估了它们的性能，并使用了盒饭研究（一项分量干预的随机对照试验）的数据。CART和CTree都能识别同质的总体子组，当子组真正存在于数据中时，相对于基于回归的方法，它们能提供更高的预测精度。CART和CTree之间的一个重要区别是，后者在构建决策树时使用正式的统计假设检验框架，这简化了识别和解释最终树模型的过程。我们还介绍了一种新的方法来可视化由决策树定义的子群。我们新颖的图形可视化为决策树识别的子群提供了更有科学意义的表征。决策树是识别由个体特征组合定义的同质子组的有用工具。虽然所有的决策树技术都会生成子组，但我们提倡使用更新的CTree技术，因为它简单且易于解释。本文的在线版本（doi:10.1186/s12982-017-0064-4）包含补充材料，可供授权用户使用。
In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However, identifying relevant subgroups can be challenging with standard statistical methods. We review the literature on decision trees, a family of techniques for partitioning the population, on the basis of covariates, into distinct subgroups who share similar values of an outcome variable. We compare two decision tree methods, the popular Classification and Regression tree (CART) technique and the newer Conditional Inference tree (CTree) technique, assessing their performance in a simulation study and using data from the Box Lunch Study, a randomized controlled trial of a portion size intervention. Both CART and CTree identify homogeneous population subgroups and offer improved prediction accuracy relative to regression-based approaches when subgroups are truly present in the data. An important distinction between CART and CTree is that the latter uses a formal statistical hypothesis testing framework in building decision trees, which simplifies the process of identifying and interpreting the final tree model. We also introduce a novel way to visualize the subgroups defined by decision trees. Our novel graphical visualization provides a more scientifically meaningful characterization of the subgroups identified by decision trees. Decision trees are a useful tool for identifying homogeneous subgroups defined by combinations of individual characteristics. While all decision tree techniques generate subgroups, we advocate the use of the newer CTree technique due to its simplicity and ease of interpretation. The online version of this article (doi:10.1186/s12982-017-0064-4) contains supplementary material, which is available to authorized users.