The performance of different propensity score methods for estimating marginal hazard ratios.
The performance of different propensity score methods for estimating marginal hazard ratios.
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DOI：
10.1002/sim.5705
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发表时间：
2013-07-20
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：
在使用观察性或非随机化数据估计治疗、暴露或干预的影响时，倾向评分方法越来越多地用于减少或最大限度地减少混杂效应。在没有未测量混杂因素的假设下，先前的研究表明倾向评分方法允许线性治疗效应的无偏估计（例如，方法或比例的差异）。然而，在生物医学研究中，事件发生时间结局经常发生。对于不同倾向评分方法用于估计治疗对至事件发生时间结局的影响的性能，缺乏研究。此外，倾向评分方法允许估计边际或人群平均治疗效果。我们进行了一系列广泛的蒙特卡罗模拟，以检查倾向评分匹配（倾向评分卡尺内的1：1贪婪最近邻匹配）、倾向评分分层、使用倾向评分的治疗加权逆概率（IPTW）以及使用倾向评分估计边际风险比的协变量调整的性能。我们发现，倾向评分匹配和使用倾向评分的IPTW都允许以最小偏差估计边际风险比。在这两种方法中，使用倾向评分的IPTW在估计治疗效果时产生的估计值均方误差较低。倾向评分分层和使用倾向评分的协变量调整导致边际和条件风险比的偏倚估计。鼓励应用研究者在估计治疗对至事件发生时间结局的相对影响时使用倾向评分匹配和使用倾向评分的IPTW。版权所有© 2012约翰威利父子有限公司.
Propensity score methods are increasingly being used to reduce or minimize the effects of confounding when estimating the effects of treatments, exposures, or interventions when using observational or non-randomized data. Under the assumption of no unmeasured confounders, previous research has shown that propensity score methods allow for unbiased estimation of linear treatment effects (e.g., differences in means or proportions). However, in biomedical research, time-to-event outcomes occur frequently. There is a paucity of research into the performance of different propensity score methods for estimating the effect of treatment on time-to-event outcomes. Furthermore, propensity score methods allow for the estimation of marginal or population-average treatment effects. We conducted an extensive series of Monte Carlo simulations to examine the performance of propensity score matching (1:1 greedy nearest-neighbor matching within propensity score calipers), stratification on the propensity score, inverse probability of treatment weighting (IPTW) using the propensity score, and covariate adjustment using the propensity score to estimate marginal hazard ratios. We found that both propensity score matching and IPTW using the propensity score allow for the estimation of marginal hazard ratios with minimal bias. Of these two approaches, IPTW using the propensity score resulted in estimates with lower mean squared error when estimating the effect of treatment in the treated. Stratification on the propensity score and covariate adjustment using the propensity score result in biased estimation of both marginal and conditional hazard ratios. Applied researchers are encouraged to use propensity score matching and IPTW using the propensity score when estimating the relative effect of treatment on time-to-event outcomes. Copyright © 2012 John Wiley & Sons, Ltd.
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影响因子：
2
作者：
Austin, Peter C.
通讯作者：
Austin, Peter C.
影响因子：
5.5
作者：
Cummings, P;McKnight, B;Greenland, S
通讯作者：
Greenland, S
影响因子：
2
作者：
Austin, Peter C.
通讯作者：
Austin, Peter C.
DOI：
10.1080/03610910903528301
发表时间：
2010-01-01
影响因子：
0.9
作者：
Austin, Peter C.
通讯作者：
Austin, Peter C.
影响因子：
1.7
作者：
Austin, Peter C.
通讯作者：
Austin, Peter C.