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The effect of a constraint on the maximum number of controls matched to each treated subject on the performance of full matching on the propensity score when estimating risk differences
Statistics in Medicine ( IF 1.8 ) Pub Date : 2020-10-07 , DOI: 10.1002/sim.8764
Peter C Austin 1, 2, 3 , Elizabeth A Stuart 4, 5, 6
Affiliation  

Many observational studies estimate causal effects using methods based on matching on the propensity score. Full matching on the propensity score is an effective and flexible method for utilizing all available data and for creating well‐balanced treatment and control groups. An important component of the full matching algorithm is the decision about whether to impose a restriction on the maximum ratio of controls matched to each treated subject. Despite the possible effect of this restriction on subsequent inferences, this issue has not been examined. We used a series of Monte Carlo simulations to evaluate the effect of imposing a restriction on the maximum ratio of controls matched to each treated subject when estimating risk differences. We considered full matching both with and without a caliper restriction. When using full matching with a caliper restriction, the imposition of a subsequent constraint on the maximum ratio of the number of controls matched to each treated subject had no effect on the quality of inferences. However, when using full matching without a caliper restriction, the imposition of a constraint on the maximum ratio of the number of controls matched to each treated subject tended to result in an increase in bias in the estimated risk difference. However, this increase in bias tended to be accompanied by a corresponding decrease in the sampling variability of the estimated risk difference. We illustrate the consequences of these restrictions using observational data to estimate the effect of medication prescribing on survival following hospitalization for a heart attack.

中文翻译:

估计风险差异时,约束对与每个接受治疗的受试者匹配的最大对照数的影响对倾向得分上完全匹配的表现的影响

许多观察性研究使用基于倾向得分匹配的方法来估计因果关系。倾向评分的完全匹配是一种有效且灵活的方法,可以利用所有可用数据并创建均衡的治疗和对照组。完全匹配算法的重要组成部分是决定是否对与每个治疗对象匹配的对照的最大比例施加限制。尽管此限制可能对后续推断产生影响,但尚未研究此问题。在评估风险差异时,我们使用了一系列的蒙特卡洛模拟来评估对与每个治疗对象匹配的对照的最大比例施加限制的效果。我们考虑了有和没有卡尺限制的完全匹配。当使用带有卡尺限制的完全匹配时,对与每个接受治疗的对象匹配的控件数量的最大比率施加后续约束不会对推断质量产生影响。然而,当使用没有卡尺限制的完全匹配时,对与每个治疗对象匹配的对照数量的最大比率施加限制倾向于导致估计风险差异的偏差增加。但是,这种偏见的增加往往伴随着估计风险差异的抽样变异性相应降低。我们使用观察性数据来估计这些限制的后果,以估计因心脏病发作住院治疗后处方药对生存的影响。对与每个接受治疗的受试者匹配的对照数量的最大比例施加随后的限制对推断的质量没有影响。然而,当使用没有卡尺限制的完全匹配时,对与每个治疗对象匹配的对照数量的最大比率施加限制倾向于导致估计风险差异的偏差增加。但是,这种偏见的增加往往伴随着估计风险差异的抽样变异性相应降低。我们使用观察性数据来估计这些限制的后果,以估计因心脏病发作住院治疗后处方药对生存的影响。对与每个接受治疗的受试者匹配的对照数量的最大比例施加随后的限制对推断的质量没有影响。然而,当使用没有卡尺限制的完全匹配时,对与每个治疗对象匹配的对照数量的最大比率施加限制倾向于导致估计风险差异的偏差增加。但是,这种偏见的增加往往伴随着估计风险差异的抽样变异性相应降低。我们使用观察性数据来估计这些限制的后果,以估计因心脏病发作住院治疗后处方药对生存的影响。
更新日期:2020-10-08
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