Propensity scores in cardiovascular research
Propensity scores in cardiovascular research
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DOI：
10.1161/circulationaha.105.594952
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发表时间：
2007-05-01
期刊：
影响因子：
37.8
通讯作者：
D'Agostino, Ralph B., Jr.
中科院分区：
文献类型：
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作者：
D'Agostino, Ralph B., Jr.
作为平衡评分5，以“平衡”给药组和对照组中协变量的分布。使用倾向评分进行匹配、分层或回归（协方差）调整，可用于产生治疗效应的无偏估计值，并在组间建立协变量平衡。在某些方法中，倾向评分本身在分析中作为权重或因子（回归调整）使用，而在其他方法中，倾向评分用于构建适当的比较（分层或匹配），但不直接用于分析。在实践中，倾向评分建模的成功与否取决于使用后治疗组之间是否达到协变量值的平衡。因此，与大多数传统设置相比，模型中可以更自由地包含协变量。例如，P值大于0.05的协变量可以包括在倾向评分模型中。关于模型中可纳入的协变量数量的一个限制是，对于纳入的每个协变量，每个治疗组中需要有足够数量的受试者。例如，如果一项研究包括30名接受治疗的个体和50名未接受治疗的个体，则倾向评分模型应包含远少于30个协变量。一旦模型拟合，评价特定倾向评分模型成功的一种方法是比较倾向评分调整前后治疗组和对照组中观察到的协变量存在的偏倚（或不平衡）量。倾向性评分的一个优点是，如果发现2名受试者，治疗组1名受试者和对照组1名受试者具有相同的倾向性评分，则可以想象这2名受试者被“随机”分配到每组，即接受治疗或对照的可能性相等。由于倾向评分仅用观察到的协变量进行估计，因此必须假设未观察到的协变量即使被测量也不会改变模型。当该假设为真时，可以相当确信可以获得治疗效果的近似无偏估计值。在建立倾向评分模型时，仅应纳入治疗前发生的协变量。如果包括治疗后测量的协变量，则倾向评分模型可以解释部分治疗效果本身。例如，如果希望在观察性研究中比较β受体阻滞剂与血管紧张素转换酶抑制剂的影响，则倾向评分模型可以包括年龄、吸烟状况和既往病史。但是，不应纳入治疗开始后测量的患者特征，例如治疗后（例如，开始使用β受体阻滞剂后）测量的射血分数。事实上，射血分数可能确实不平衡，
as a balancing score5 to “balance” the distribution of the covariates in the treated and control groups. Matching, stratification, or regression (covariance) adjustment with the propensity score can be used to produce unbiased estimates of the treatment effects and create covariate balance between groups. In some of these methods, the propensity score itself is used in the analyses as a weight or factor (regression adjustment), whereas in others it is used to construct the appropriate comparisons (stratification or matching) but not in the analyses directly.In practice, the success of propensity score modeling is judged by whether balance on covariate values is achieved between the treatment groups after its use. Because of this, one can be more liberal with inclusion of covariates in the model than in most traditional settings. For instance, covariates with P values larger than 0.05 can be included in the propensity score model. One limitation that concerns the number of covariates that can be included in the model is that there needs to be a sufficient number of participants in each treatment group for each covariate that is included. For instance, if a study includes 30 treated and 50 untreated individuals, the propensity score model should have much less than 30 covariates included. Once the model is fit, one method to evaluate the success of a particular propensity score model is to compare the amount of bias (or imbalance) that existed on observed covariates in the treated and control groups before and after adjustment for propensity scores. One advantage of propensity scores is that if 2 subjects are found, 1 subject in the treated group and 1 subject in the control, with the same propensity score, then one could imagine that these 2 subjects were “randomly” assigned to each group in the sense of being equally likely to be treated or control. Because propensity scores are estimated with only observed covariates, one has to assume that unobserved covariates would not have changed the model had they been measured. When this assumption is true, one can be fairly confident that approximately unbiased estimates for the treatment effect can be obtained. When building the propensity score model, only covariates that occur pretreatment should be included. If one includes covariates that are measured posttreatment, then the propensity score model may explain part of the treatment effect itself. For example, if one wished to compare in an observational study the impact of a ß-blocker versus an angiotensinconverting enzyme inhibitor, the propensity score model could include age, smoking status, and prior medical history. However, patient characteristics measured after the treatment began, such as an ejection fraction measurement taken posttreatment (eg, after ß-blocker initiation) should not be included. Indeed, ejection fraction may indeed be imbalanced