Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.
Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.
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DOI：
10.1016/j.jclinepi.2009.11.020
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发表时间：
2010-08
影响因子：
7.2
通讯作者：
Funk, Michele Jonsson
中科院分区：
文献类型：
--
作者：
Westreich, Daniel;Lessler, Justin;Funk, Michele Jonsson
关键词：
观察数据分析的倾向得分通常使用逻辑回归估计。我们在这篇综述中的目标是评估逻辑回归的机器学习替代方案，这些替代方案可能实现相同的目标，但假设更少或准确性更高。我们从公共卫生、生物统计学、离散数学和计算机科学文献中确定了倾向得分估计和/或分类的替代方法，并评估了这些算法对倾向得分估计问题的适用性、相对于逻辑回归的潜在优势以及易用性。我们确定了四种技术作为逻辑回归的替代品：神经网络、支持向量机、决策树（CART）和元分类器（特别是增强）。虽然逻辑回归的假设被很好地理解，但这些假设经常被忽略。与逻辑回归相比，这四种方法各有优缺点。增强（元分类器）和较小程度的决策树（特别是CART）似乎最有希望在倾向评分分析的背景下使用，但需要广泛的模拟研究来建立它们在实践中的效用。
Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this Review was to assess machine learning alternatives to logistic regression which may accomplish the same goals but with fewer assumptions or greater accuracy. We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (CART), and meta-classifiers (in particular, boosting). While the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and to a lesser extent decision trees (particularly CART) appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice.
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