Prognostic score-based balance measures can be a useful diagnostic for propensity score methods in comparative effectiveness research.
Prognostic score-based balance measures can be a useful diagnostic for propensity score methods in comparative effectiveness research.
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DOI：
10.1016/j.jclinepi.2013.01.013
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发表时间：
2013-08
影响因子：
7.2
通讯作者：
Leacy, Finbarr P.
中科院分区：
文献类型：
--
作者：
Stuart, Elizabeth A.;Lee, Brian K.;Leacy, Finbarr P.
关键词：
检查协变量平衡是确定倾向评分方法何时成功减少偏倚的规定方法。本研究评估了各种平衡指标的性能，包括基于预后评分（也称为疾病风险评分）的拟议平衡指标，以确定哪些平衡指标与治疗效果估计值中的偏倚最相关。计算了多个共同平衡指标与治疗效果估计偏倚的相关性，治疗效果估计偏倚通过比值加权、倾向评分亚分类和倾向评分完全匹配产生。根据实际数据设置，使用模拟数据。设置包括连续和二元协变量以及仅连续协变量。预后评分的标准化平均差、平均标准化平均差和平均t统计量均与效应估计的偏倚具有高度相关性。总体而言，预后评分显示所有考虑的平衡措施的相关性最高。预后评分指标的性能一般不受模型误设定的影响，并在各种情况下表现良好。研究人员应考虑使用基于预后评分的平衡措施来评估倾向评分方法在非实验性研究中减少偏倚的性能。
Examining covariate balance is the prescribed method for determining when propensity score methods are successful at reducing bias. This study assessed the performance of various balance measures, including a proposed balance measure based on the prognostic score (also known as the disease-risk score), to determine which balance measures best correlate with bias in the treatment effect estimate. The correlations of multiple common balance measures with bias in the treatment effect estimate produced by weighting by the odds, subclassification on the propensity score, and full matching on the propensity score were calculated. Simulated data were used, based on realistic data settings. Settings included both continuous and binary covariates and continuous covariates only. The standardized mean difference in prognostic scores, the mean standardized mean difference, and the mean t-statistic all had high correlations with bias in the effect estimate. Overall, prognostic scores displayed the highest correlations of all the balance measures considered. Prognostic score measure performance was generally not affected by model misspecification and performed well under a variety of scenarios. Researchers should consider using prognostic score–based balance measures for assessing the performance of propensity score methods for reducing bias in non-experimental studies.
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影响因子：
2.6
作者：
Belitser, Svetlana V.;Martens, Edwin P.;Klungel, Olaf H.
通讯作者：
Klungel, Olaf H.
影响因子：
2.6
作者：
Glynn, Robert J.;Gagne, Joshua J.;Schneeweiss, Sebastian
通讯作者：
Schneeweiss, Sebastian
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：
Arbogast, Patrick G.;Ray, Wayne A.
通讯作者：
Ray, Wayne A.
DOI：
10.1097/ede.0b013e3181a663cc
发表时间：
2009-07
期刊：
Epidemiology (Cambridge, Mass.)
影响因子：
--
作者：
Schneeweiss S;Rassen JA;Glynn RJ;Avorn J;Mogun H;Brookhart MA
通讯作者：
Brookhart MA