首页> 外文期刊>Pharmacoepidemiology and drug safety >Comparison of the ability of double‐robust estimators to correct bias in propensity score matching analysis. A M A M onte C C arlo simulation study
【24h】

Comparison of the ability of double‐robust estimators to correct bias in propensity score matching analysis. A M A M onte C C arlo simulation study

机译:双重稳压估计能力比较倾向分数匹配分析中校正偏差的能力。 M A M ANTE C C C C ARLO仿真研究

获取原文
获取原文并翻译 | 示例
获取外文期刊封面目录资料

摘要

Abstract Objective As covariates are not always adequately balanced after propensity score matching and double‐ adjustment can be used to remove residual confounding, we compared the performance of several double‐robust estimators in different scenarios. Methods We conducted a series of Monte Carlo simulations on virtual observational studies. After estimating the propensity scores by logistic regression, we performed 1:1 optimal, nearest‐neighbor, and caliper matching. We used 4 estimators on each matched sample: (1) a crude estimator without double‐adjustment, (2) double‐adjustment for the propensity scores, (3) double‐adjustment for the unweighted unbalanced covariates, and (4) double‐adjustment for the unbalanced covariates, weighted by their strength of association with the outcome. Results The crude estimator led to highest bias in all tested scenarios. Double‐adjustment for the propensity scores effectively removed confounding only when the propensity score models were correctly specified. Double‐adjustment for the unbalanced covariates was more robust to misspecification. Double‐adjustment for the weighted unbalanced covariates outperformed the other approaches in every scenario and using any matching algorithm, as measured by the mean squared error. Conclusion Double‐adjustment can be used to remove residual confounding after propensity score matching. The unbalanced covariates with the strongest confounding effects should be adjusted.
机译:摘要目的随着协调因子并不总是充分平衡,在倾向得分匹配和双重调整可用于去除剩余混淆后,我们比较了不同场景中几种双重强大估计器的性能。方法我们在虚拟观测研究中进行了一系列蒙特卡罗模拟。通过Logistic回归估算倾向分数后,我们执行了1:1最佳,最近邻居和卡尺匹配。我们在每个匹配的样品上使用了4个估算器:(1)粗估算器没有双重调整,(2)对倾向分数进行双重调整,(3)对未加权的不平衡协变量进行双重调整,(4)双调整对于不平衡的协变量,通过他们与结果的关联强度加权。结果原油估计器导致所有测试方案中的最高偏差。只有在正确指定倾向评分模型时,倾向的双重调整才会有效地消除了混杂。对不平衡协变量的双重调整更加强大地拼盘。加权不平衡协变量的双重调整优于各种场景中的其他方法,并使用任何匹配算法,通过平均平方误差测量。结论双重调整可用于去除倾向分数匹配后的残余混杂。应调整最强烈的混杂效果的不平衡协变量。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号