Double propensity-score adjustment: A solution to design bias or bias due to incomplete matching.
Double propensity-score adjustment: A solution to design bias or bias due to incomplete matching.
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DOI：
10.1177/0962280214543508
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发表时间：
2017-02
影响因子：
2.3
通讯作者：
Austin PC
中科院分区：
文献类型：
--
作者：
Austin PC
当使用观察数据估计治疗效果时，倾向评分匹配经常用于减少混杂效应。匹配允许一个估计的平均效果的治疗。Rosenbaum和Rubin创造了“不完全匹配导致的偏倚”一词，用于描述由于没有合适的对照受试者而将一些接受治疗的受试者从匹配样本中排除时可能发生的偏倚。不完全匹配的存在提出了关于估计治疗效果对整个治疗受试者人群的普遍性的重要问题。我们描述了一个分析解决方案，以解决由于不完全匹配的偏见。我们的方法是基于使用最佳或最近邻匹配，而不是卡尺匹配（这经常导致排除一些治疗对象）。在倾向评分匹配的样本内，然后使用倾向评分进行协变量调整，以插补每例接受治疗的受试者在缺乏治疗的情况下缺失的潜在结局。使用蒙特卡罗模拟，我们发现，所提出的方法导致估计的治疗效果，基本上是无偏的。与单独的井径匹配相比，以及与单独的最佳匹配或最近邻匹配相比，该方法导致偏差降低。单独的卡尺匹配导致设计偏倚或由于不完全匹配导致的偏倚，而单独的最佳匹配或最近邻匹配导致由于残留混杂导致的偏倚。所提出的方法也往往会导致估计值的均方误差降低相比，当使用卡尺匹配。
Propensity-score matching is frequently used to reduce the effects of confounding when using observational data to estimate the effects of treatments. Matching allows one to estimate the average effect of treatment in the treated. Rosenbaum and Rubin coined the term “bias due to incomplete matching” to describe the bias that can occur when some treated subjects are excluded from the matched sample because no appropriate control subject was available. The presence of incomplete matching raises important questions around the generalizability of estimated treatment effects to the entire population of treated subjects. We describe an analytic solution to address the bias due to incomplete matching. Our method is based on using optimal or nearest neighbor matching, rather than caliper matching (which frequently results in the exclusion of some treated subjects). Within the sample matched on the propensity score, covariate adjustment using the propensity score is then employed to impute missing potential outcomes under lack of treatment for each treated subject. Using Monte Carlo simulations, we found that the proposed method resulted in estimates of treatment effect that were essentially unbiased. This method resulted in decreased bias compared to caliper matching alone and compared to either optimal matching or nearest neighbor matching alone. Caliper matching alone resulted in design bias or bias due to incomplete matching, while optimal matching or nearest neighbor matching alone resulted in bias due to residual confounding. The proposed method also tended to result in estimates with decreased mean squared error compared to when caliper matching was used.