The performance of inverse probability of treatment weighting and full matching on the propensity score in the presence of model misspecification when estimating the effect of treatment on survival outcomes.
The performance of inverse probability of treatment weighting and full matching on the propensity score in the presence of model misspecification when estimating the effect of treatment on survival outcomes.
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在估计治疗对生存结果的影响时，在存在模型错误指定的情况下，在存在模型错误的情况下，对治疗加权的反比概率和对倾向得分的全面匹配的表现。
DOI：
10.1177/0962280215584401
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发表时间：
2017-08
影响因子：
2.3
通讯作者：
Stuart EA
中科院分区：
文献类型：
--
作者：
Austin PC;Stuart EA
有越来越多的兴趣，估计因果关系的影响，使用观察数据的治疗。在观察性研究中，倾向评分匹配方法经常用于调整治疗组和对照组个体之间观察到的特征差异。在医学文献中，生存率或至事件发生时间结局经常出现，但在生存分析中使用倾向评分方法尚未得到彻底研究。本文比较了两种估计平均治疗效应（ATE）对生存结局的影响的方法：治疗加权逆概率（IPTW）和完全匹配。这些方法的性能进行了比较，在一个广泛的模拟，不同程度的混淆和数量的倾向评分模型的误指定。我们发现，当ATE是目标被估量，治疗选择过程是弱至中度时，IPTW和完全匹配均导致边缘风险比的估计，偏倚可忽略不计。然而，当治疗选择过程很强时，即使正确指定了倾向评分模型，这两种方法也会导致对真实边际风险比的偏倚估计。当正确指定倾向评分模型时，完全匹配的偏倚往往低于IPTW。这些偏差和两种方法之间的差异的原因似乎是由于每种方法产生的一些极端权重。随着协变量对治疗选择的影响程度增加，这两种方法都倾向于产生更极端的权重。此外，IPTW比完全匹配观察到更多的极端权重。然而，当正确指定倾向评分模型时，通过使用具有限制的IPTW和与卡尺限制的完全匹配，缓解了存在强治疗选择过程时两种方法的较差性能。
There is increasing interest in estimating the causal effects of treatments using observational data. Propensity-score matching methods are frequently used to adjust for differences in observed characteristics between treated and control individuals in observational studies. Survival or time-to-event outcomes occur frequently in the medical literature, but the use of propensity score methods in survival analysis has not been thoroughly investigated. This paper compares two approaches for estimating the Average Treatment Effect (ATE) on survival outcomes: Inverse Probability of Treatment Weighting (IPTW) and full matching. The performance of these methods was compared in an extensive set of simulations that varied the extent of confounding and the amount of misspecification of the propensity score model. We found that both IPTW and full matching resulted in estimation of marginal hazard ratios with negligible bias when the ATE was the target estimand and the treatment-selection process was weak to moderate. However, when the treatment-selection process was strong, both methods resulted in biased estimation of the true marginal hazard ratio, even when the propensity score model was correctly specified. When the propensity score model was correctly specified, bias tended to be lower for full matching than for IPTW. The reasons for these biases and for the differences between the two methods appeared to be due to some extreme weights generated for each method. Both methods tended to produce more extreme weights as the magnitude of the effects of covariates on treatment selection increased. Furthermore, more extreme weights were observed for IPTW than for full matching. However, the poorer performance of both methods in the presence of a strong treatment-selection process was mitigated by the use of IPTW with restriction and full matching with a caliper restriction when the propensity score model was correctly specified.
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影响因子：
2
作者：
Austin PC;Stuart EA
通讯作者：
Stuart EA
影响因子：
2
作者：
Austin, Peter C.;Small, Dylan S.
通讯作者：
Small, Dylan 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：
2
作者：
Austin, Peter C.
通讯作者：
Austin, Peter C.