The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies.
The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies.
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DOI：
10.1002/sim.3854
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发表时间：
2010-09-10
影响因子：
2
通讯作者：
Austin, Peter C.
中科院分区：
文献类型：
--
作者：
Austin, Peter C.
关键词：
倾向评分方法越来越多地被用于使用观察数据估计治疗对健康结果的影响。使用倾向评分估计治疗效果的方法有四种：使用倾向评分的协变量调整、倾向评分分层、倾向评分匹配和使用倾向评分的治疗加权逆概率（IPTW）。当结果是二元的，治疗对结果的影响可以用比值比，相对风险，风险差异或需要治疗的数量来描述，一些临床评论员认为，风险差异和需要治疗的数量比比值比或相对风险对临床决策更有意义。然而，有一个不同的倾向评分方法估计风险差异的相对性能的信息匮乏。我们进行了一系列蒙特卡罗模拟来研究这个问题。我们检查了偏差，方差估计，置信区间的覆盖范围，均方误差（MSE）和I型错误率。一个双重的强大版本的IPTW有上级性能相比，其他倾向评分方法。它导致了风险差异的无偏估计，具有最低标准误差的治疗效果，具有正确覆盖率的置信区间，以及正确的I类错误率。分层、倾向评分匹配和使用倾向评分的协变量调整导致估计风险差异的轻微至中度偏倚。与其他倾向评分方法相比，基于IPTW的估计量具有较低的MSE。IPTW和倾向评分匹配之间的差异可能反映了这两种方法分别估计了平均治疗效果和平均治疗效果。版权所有© 2010约翰威利父子有限公司.
Propensity score methods are increasingly being used to estimate the effects of treatments on health outcomes using observational data. There are four methods for using the propensity score to estimate treatment effects: covariate adjustment using the propensity score, stratification on the propensity score, propensity-score matching, and inverse probability of treatment weighting (IPTW) using the propensity score. When outcomes are binary, the effect of treatment on the outcome can be described using odds ratios, relative risks, risk differences, or the number needed to treat. Several clinical commentators suggested that risk differences and numbers needed to treat are more meaningful for clinical decision making than are odds ratios or relative risks. However, there is a paucity of information about the relative performance of the different propensity-score methods for estimating risk differences. We conducted a series of Monte Carlo simulations to examine this issue. We examined bias, variance estimation, coverage of confidence intervals, mean-squared error (MSE), and type I error rates. A doubly robust version of IPTW had superior performance compared with the other propensity-score methods. It resulted in unbiased estimation of risk differences, treatment effects with the lowest standard errors, confidence intervals with the correct coverage rates, and correct type I error rates. Stratification, matching on the propensity score, and covariate adjustment using the propensity score resulted in minor to modest bias in estimating risk differences. Estimators based on IPTW had lower MSE compared with other propensity-score methods. Differences between IPTW and propensity-score matching may reflect that these two methods estimate the average treatment effect and the average treatment effect for the treated, respectively. Copyright © 2010 John Wiley & Sons, Ltd.
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影响因子：
2
作者：
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