Propensity Score Modeling in Electronic Health Records with Time-to-Event Endpoints: Application to Kidney Transplantation

Jonathan W. Yu, D. Bandyopadhyay, Shu Yang, Le Kang, G. Gupta
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引用次数: 1

Abstract

For large observational studies lacking a control group (unlike randomized controlled trials, RCT), propensity scores (PS) are often the method of choice to account for pre-treatment confounding in baseline characteristics, and thereby avoid substantial bias in treatment estimation. A vast majority of PS techniques focus on average treatment effect estimation, without any clear consensus on how to account for confounders, especially in a multiple treatment setting. Furthermore, for time-to event outcomes, the analytical framework is further complicated in presence of high censoring rates (sometimes, due to non-susceptibility of study units to a disease), imbalance between treatment groups, and clustered nature of the data (where, survival outcomes appear in groups). Motivated by a right-censored kidney transplantation dataset derived from the United Network of Organ Sharing (UNOS), we investigate and compare two recent promising PS procedures, (a) the generalized boosted model (GBM), and (b) the covariate-balancing propensity score (CBPS), in an attempt to decouple the causal effects of treatments (here, study subgroups, such as hepatitis C virus (HCV) positive/negative donors, and positive/negative recipients) on time to death of kidney recipients due to kidney failure, post transplantation. For estimation, we employ a 2-step procedure which addresses various complexities observed in the UNOS database within a unified paradigm. First, to adjust for the large number of confounders on the multiple sub-groups, we fit multinomial PS models via procedures (a) and (b). In the next stage, the estimated PS is incorporated into the likelihood of a semi-parametric cure rate Cox proportional hazard frailty model via inverse probability of treatment weighting, adjusted for multi-center clustering and excess censoring, Our data analysis reveals a more informative and superior performance of the full model in terms of treatment effect estimation, over sub-models that relaxes the various features of the event time dataset.
以时间到事件为终点的电子健康记录中的倾向评分模型:在肾移植中的应用
对于缺乏对照组的大型观察性研究(不像随机对照试验,RCT),倾向评分(PS)通常是考虑基线特征的治疗前混淆的选择方法,从而避免治疗估计中的重大偏差。绝大多数PS技术侧重于平均治疗效果估计,对于如何考虑混杂因素没有任何明确的共识,特别是在多重治疗环境中。此外,对于时间到事件的结果,由于存在高审查率(有时,由于研究单位对某种疾病不敏感)、治疗组之间的不平衡以及数据的聚集性(其中,生存结果出现在组中),分析框架进一步复杂化。受来自器官共享联合网络(UNOS)的右审查肾移植数据集的激励,我们调查并比较了两种最近有前途的PS程序,(a)广义增强模型(GBM)和(b)协变量平衡倾向评分(CBPS),试图解解治疗的因果效应(这里,研究亚组,如丙型肝炎病毒(HCV)阳性/阴性供者,和阳性/阴性受者)及时死亡肾受者因肾功能衰竭,移植后。对于估计,我们采用了一个两步程序,在统一的范例中解决UNOS数据库中观察到的各种复杂性。首先,为了调整多个子组上的大量混杂因素,我们通过程序(a)和(b)拟合多项PS模型。在下一阶段,估计的PS通过处理权重的逆概率纳入半参数治愈率Cox比例风险脆弱性模型的可能性,并根据多中心聚类和过度审查进行调整。我们的数据分析显示,在治疗效果估计方面,与放松事件时间数据集的各种特征的子模型相比,完整模型具有更丰富的信息和更优越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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