Currently, methods for conducting multiple treatment propensity scoring in the presence of high-dimensional covariate spaces that result from "big data" are lacking-the most prominent method relies on inverse probability treatment weighting (IPTW). However, IPTW only utilizes one element of the generalized propensity score (GPS) vector, which can lead to a loss of information and inadequate covariate balance in the presence of multiple treatments. This limitation motivates the development of a novel propensity score method that uses the entire GPS vector to establish a scalar balancing score that, when adjusted for, achieves covariate balance in the presence of potentially high-dimensional covariates. Specifically, the generalized propensity score cumulative distribution function (GPS-CDF) method is introduced. A one-parameter power function fits the CDF of the GPS vector and a resulting scalar balancing score is used for matching and/or stratification. Simulation results show superior performance of the new method compared to IPTW both in achieving covariate balance and estimating average treatment effects in the presence of multiple treatments. The proposed approach is applied to a study derived from electronic medical records to determine the causal relationship between three different vasopressors and mortality in patients with non-traumatic aneurysmal subarachnoid hemorrhage. Results suggest that the GPS-CDF method performs well when applied to large observational studies with multiple treatments that have large covariate spaces.

中文翻译：

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多种治疗倾向评分匹配和分层的新方法：应用于电子健康记录衍生研究。


目前，缺乏在“大数据”产生的高维协变量空间的情况下进行多种治疗倾向评分的方法——最突出的方法依赖于逆概率治疗加权（IPTW）。然而，IPTW 仅利用广义倾向评分 (GPS) 向量的一个元素，这可能会导致信息丢失和在存在多种治疗的情况下协变量平衡不足。这一限制促使开发一种新颖的倾向评分方法，该方法使用整个 GPS 向量来建立标量平衡评分，经过调整后，可以在存在潜在高维协变量的情况下实现协变量平衡。具体来说，引入了广义倾向得分累积分布函数（GPS-CDF）方法。单参数幂函数拟合 GPS 向量的 CDF，所得标量平衡分数用于匹配和/或分层。模拟结果表明，与 IPTW 相比，新方法在实现协变量平衡和估计多种治疗的平均治疗效果方面均具有优越的性能。所提出的方法应用于一项源自电子病历的研究，以确定三种不同的血管升压药与非创伤性动脉瘤性蛛网膜下腔出血患者死亡率之间的因果关系。结果表明，当应用于具有较大协变量空间的多种治疗的大型观察研究时，GPS-CDF 方法表现良好。