标题： 精确匹配作为倾向得分匹配的替代方法 显示英文标题

标题： Exact matching as an alternative to propensity score matching

摘要： 从观察性研究或不同临床研究中比较不同的医疗治疗方法，常常受到混杂因素的偏倚，例如患者人口统计学特征或试验纳入标准的系统性差异。 倾向得分匹配是一种调整此类混杂因素的常用方法。 它比较的是患者反应的加权平均值。 权重是从逻辑回归模型中计算出来的，目的是减少治疗组之间混杂因素的差异。 然而，这些组只是“大致匹配”，没有普遍接受的原则来确定何时匹配是“足够好”的。 在本文中，我们通过将其视为一个约束优化问题，提出了另一种解决匹配问题的方法。 我们研究了精确匹配的条件，即在匹配后，治疗组中混杂因素的平均值相同。 我们的方法类似于Signorovitch等人(2010)提出的匹配调整间接比较方法，但有两个主要区别：首先，我们不对匹配权重施加任何特定的功能形式；其次，所提出的方法可以应用于来自多个治疗组的个体患者数据，以及个体患者数据和汇总数据的混合。 显示英文摘要

摘要： The comparison of different medical treatments from observational studies or across different clinical studies is often biased by confounding factors such as systematic differences in patient demographics or in the inclusion criteria for the trials. Propensity score matching is a popular method to adjust for such confounding. It compares weighted averages of patient responses. The weights are calculated from logistic regression models with the intention to reduce differences between the confounders in the treatment groups. However, the groups are only "roughly matched" with no generally accepted principle to determine when a match is "good enough". In this manuscript, we propose an alternative approach to the matching problem by considering it as a constrained optimization problem. We investigate the conditions for exact matching in the sense that the average values of confounders are identical in the treatment groups after matching. Our approach is similar to the matching-adjusted indirect comparison approach by Signorovitch et al. (2010) but with two major differences: First, we do not impose any specific functional form on the matching weights; second, the proposed approach can be applied to individual patient data from several treatment groups as well as to a mix of individual patient and aggregated data.