Improving propensity score weighting using machine learning.
Improving propensity score weighting using machine learning.
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DOI：
10.1002/sim.3782
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发表时间：
2010-02-10
影响因子：
2
通讯作者：
Stuart, Elizabeth A.
中科院分区：
文献类型：
--
作者：
Lee, Brian K.;Lessler, Justin;Stuart, Elizabeth A.
机器学习技术，如分类和回归树（CART）已被认为是估计倾向分数的逻辑回归的有前途的替代方案。作者使用模拟数据检查了各种基于CART的倾向评分模型的性能。不同样本量的假设研究（n=500，1000，2000）与二进制曝光，连续的结果，和10个协变量进行了模拟，根据协变量和曝光之间的非线性和非加性关联的程度不同的七个场景。使用逻辑回归（所有主效应）、CART、修剪CART以及袋装CART、随机森林和增强CART的集成方法估计倾向评分权重。性能指标包括协变量平衡、标准误、绝对偏倚百分比和95%置信区间覆盖率。所有方法在非线性或非相加性单独条件下均表现出普遍可接受的性能。然而，在中度非加性和中度非线性条件下，逻辑回归的性能低于标准，而集成方法提供了更好的偏倚减少和更一致的95% CI覆盖率。结果表明，集成方法，特别是增强CART，可能是有用的倾向评分加权。
Machine learning techniques such as classification and regression trees (CART) have been suggested as promising alternatives to logistic regression for the estimation of propensity scores. The authors examined the performance of various CART-based propensity score models using simulated data. Hypothetical studies of varying sample sizes (n=500, 1000, 2000) with a binary exposure, continuous outcome, and ten covariates were simulated under seven scenarios differing by degree of non-linear and non-additive associations between covariates and the exposure. Propensity score weights were estimated using logistic regression (all main effects), CART, pruned CART, and the ensemble methods of bagged CART, random forests, and boosted CART. Performance metrics included covariate balance, standard error, percent absolute bias, and 95% confidence interval coverage. All methods displayed generally acceptable performance under conditions of either non-linearity or non-additivity alone. However, under conditions of both moderate non-additivity and moderate non-linearity, logistic regression had subpar performance, while ensemble methods provided substantially better bias reduction and more consistent 95% CI coverage. The results suggest that ensemble methods, especially boosted CART, may be useful for propensity score weighting.
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