Propensity-score matching with competing risks in survival analysis.
Propensity-score matching with competing risks in survival analysis.
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DOI：
10.1002/sim.8008
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发表时间：
2019-02-28
影响因子：
2
通讯作者：
Fine JP
中科院分区：
文献类型：
--
作者：
Austin PC;Fine JP
倾向评分匹配是一种常用的分析方法，用于在使用观察性数据估计治疗效果时消除因测量的基线协变量造成的混杂效应。至事件发生时间结局在医学研究中很常见。竞争风险是指其发生排除了主要关注事件发生时间结局的发生的结局。所有非致死性结局和所有原因特异性死亡结局都可能存在竞争风险。在存在竞争风险的情况下，缺乏关于倾向评分匹配的指导。我们描述了当使用倾向评分与竞争风险数据匹配时，如何获得治疗效果的相对和绝对指标。可通过在匹配样本中使用病因特异性风险模型获得治疗相对效应的估计值。通过比较匹配的治疗受试者和匹配的对照受试者之间的累积发生率函数（CIF），可获得绝对治疗效应的估计值。我们进行了一系列的Monte Carlo模拟，比较经验的I类错误率的不同的统计方法来测试的平等CIF估计在匹配的样本。我们还研究了不同方法估计边际子分布风险比的性能。我们建议使用一个解释结果配对内聚类的边际子分布风险模型来检验CIF的相等性并估计子分布风险比。我们通过使用急性心肌梗死患者出院的数据来说明所描述的方法，以估计他汀类药物出院处方对心血管死亡的影响。
Propensity‐score matching is a popular analytic method to remove the effects of confounding due to measured baseline covariates when using observational data to estimate the effects of treatment. Time‐to‐event outcomes are common in medical research. Competing risks are outcomes whose occurrence precludes the occurrence of the primary time‐to‐event outcome of interest. All non‐fatal outcomes and all cause‐specific mortality outcomes are potentially subject to competing risks. There is a paucity of guidance on the conduct of propensity‐score matching in the presence of competing risks. We describe how both relative and absolute measures of treatment effect can be obtained when using propensity‐score matching with competing risks data. Estimates of the relative effect of treatment can be obtained by using cause‐specific hazard models in the matched sample. Estimates of absolute treatment effects can be obtained by comparing cumulative incidence functions (CIFs) between matched treated and matched control subjects. We conducted a series of Monte Carlo simulations to compare the empirical type I error rate of different statistical methods for testing the equality of CIFs estimated in the matched sample. We also examined the performance of different methods to estimate the marginal subdistribution hazard ratio. We recommend that a marginal subdistribution hazard model that accounts for the within‐pair clustering of outcomes be used to test the equality of CIFs and to estimate subdistribution hazard ratios. We illustrate the described methods by using data on patients discharged from hospital with acute myocardial infarction to estimate the effect of discharge prescribing of statins on cardiovascular death.
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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：
2.3
作者：
Austin PC
通讯作者：
Austin PC
影响因子：
2.3
作者：
Austin, Peter C.;Stuart, Elizabeth A.
通讯作者：
Stuart, Elizabeth A.
影响因子：
1.2
作者：
Austin, Peter C.;Laupacis, Andreas
通讯作者：
Laupacis, Andreas
影响因子：
--
作者：
Altman, DG;Andersen, PK
通讯作者：
Andersen, PK