ASYMMETRIC STRATIFICATION - AN OUTLINE FOR AN EFFICIENT METHOD FOR CONTROLLING CONFOUNDING IN COHORT STUDIES
ASYMMETRIC STRATIFICATION - AN OUTLINE FOR AN EFFICIENT METHOD FOR CONTROLLING CONFOUNDING IN COHORT STUDIES
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DOI：
10.1093/oxfordjournals.aje.a114838
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发表时间：
1988-03-01
影响因子：
5
通讯作者：
GOLDMAN, L
中科院分区：
文献类型：
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作者：
COOK, EF;GOLDMAN, L
混淆通常由交叉分层或多变量模型控制。第一种方法简单直观，但对于控制许多因素并不实用。第二种方法虽然不太直观，但可能为控制许多混杂因素提供了更有效的方法，但其控制混杂的能力取决于所选模型的适当性。基于多变量混杂因素评分或倾向评分的混合方法结合了两种方法的有利特征，可能更适合于控制许多混杂因素。然而，所得的地层是由多元模型的子范围定义的，因此可能没有什么内在意义。作者提出了不对称分层的原则，以有效地控制队列研究中的一些混杂因素，同时保留了交叉分层的直观吸引力和一般框架。提出的方法类似于倾向评分分析，但不使用多元模型来定义地层。相反，地层仅由原始潜在混杂因素的一个子集的类别来定义。作者还演示了我们提出的方法如何通过应用分类和回归树（CART）（递归划分）来实现，正如Breiman等人（分类和回归树）所概述的那样。Belmont, CA: Wadsworth, 1984)。计算机模拟和一个实际例子表明，所提出的方法是一个潜在的更简单的替代标准倾向得分分析。本文还就如何改进所提出的方法提出了具体建议。
Confounding is usually controlled by either cross-stratification or multivariate modeling. The first approach is simple and intuitive, but it is not practical for controlling many factors. The second approach, although less intuitive, may provide a more efficient means for controlling many confounders, but its ability to control confounding depends on the appropriateness of the chosen model. Hybrid methods based on a multivariate confounder score or a propensity score combine the favorable characteristics of both methods and may be better suited for controlling many confounders. However, the resulting strata are defined by subranges of a multivariate model, and, therefore, may possess little intrinsic meaning. The authors propose the principle of asymmetric stratifications to control efficiently a number of confounders in cohort studies while retaining the intuitive appeal and general framework of cross-stratification. The proposed method resembles a propensity score analysis but does not use a multivariate model to define the strata. Instead, strata are defined by the categories of only a subset of the original potential confounders. The authors also demonstrate how our proposed method can be implemented by an application of classification and regression trees (CART) (recursive partitioning), as outlined by Breiman et al. (Classification and Regression Trees. Belmont, CA: Wadsworth, 1984). Computer simulations and an actual example suggest that the proposed method is a potentially simpler alternative to the standard propensity score analysis. Specific recommendations on how the proposed method can be improved are also presented.