The use of bootstrapping when using propensity-score matching without replacement: a simulation study.
The use of bootstrapping when using propensity-score matching without replacement: a simulation study.
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DOI：
10.1002/sim.6276
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发表时间：
2014-10-30
影响因子：
2
通讯作者：
Small, Dylan S.
中科院分区：
文献类型：
--
作者：
Austin, Peter C.;Small, Dylan S.
关键词：
在使用观察数据时，倾向-得分匹配经常被用来估计治疗、暴露和干预的效果。在使用倾向-分数匹配时，一个重要的问题是如何估计估计的治疗效果的标准误差。精确的方差估计允许构造具有公告的覆盖率的置信度区间和具有正确的I类错误率的具有统计意义的检验。关于标准误差应该如何估计，文献中有不同意见。Bootstrap是一种常用的重采样方法，它允许估计被估计参数的采样变异性。Bootstrap方法很少与倾向-得分匹配结合使用。我们提出了两种不同的Bootstrap方法，用于在不使用替换的情况下使用倾向-分数匹配，并通过一系列的蒙特卡罗模拟来检验它们的性能。第一种方法包括从倾向得分匹配样本中的匹配对中提取引导样本。第二种方法包括从原始样本中提取引导样本，分别估计每个引导样本中的倾向分数，并在每个引导样本中创建匹配的样本。前一种方法得到的标准误差估计值更接近估计效果的抽样分布的经验标准差。
Propensity-score matching is frequently used to estimate the effect of treatments, exposures, and interventions when using observational data. An important issue when using propensity-score matching is how to estimate the standard error of the estimated treatment effect. Accurate variance estimation permits construction of confidence intervals that have the advertised coverage rates and tests of statistical significance that have the correct type I error rates. There is disagreement in the literature as to how standard errors should be estimated. The bootstrap is a commonly used resampling method that permits estimation of the sampling variability of estimated parameters. Bootstrap methods are rarely used in conjunction with propensity-score matching. We propose two different bootstrap methods for use when using propensity-score matching without replacementand examined their performance with a series of Monte Carlo simulations. The first method involved drawing bootstrap samples from the matched pairs in the propensity-score-matched sample. The second method involved drawing bootstrap samples from the original sample and estimating the propensity score separately in each bootstrap sample and creating a matched sample within each of these bootstrap samples. The former approach was found to result in estimates of the standard error that were closer to the empirical standard deviation of the sampling distribution of estimated effects.
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影响因子：
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作者：
Austin, Peter C.
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