He Kevin, Li Yun, Rao Panduranga S, Sung Randall S, Schaubel Douglas E
Department of Biostatistics, University of Michigan, 1415 Washington Hts., Ann Arbor, MI, 48109-2029, USA.
Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA, 19104, USA.
Lifetime Data Anal. 2020 Jul;26(3):451-470. doi: 10.1007/s10985-019-09485-x. Epub 2019 Oct 1.
In evaluating the benefit of a treatment on survival, it is often of interest to compare post-treatment survival with the survival function that would have been observed in the absence of treatment. In many practical settings, treatment is time-dependent in the sense that subjects typically begin follow-up untreated, with some going on to receive treatment at some later time point. In observational studies, treatment is not assigned at random and, therefore, may depend on various patient characteristics. We have developed semi-parametric matching methods to estimate the average treatment effect on the treated (ATT) with respect to survival probability and restricted mean survival time. Matching is based on a prognostic score which reflects each patient's death hazard in the absence of treatment. Specifically, each treated patient is matched with multiple as-yet-untreated patients with similar prognostic scores. The matched sets do not need to be of equal size, since each matched control is weighted in order to preserve risk score balancing across treated and untreated groups. After matching, we estimate the ATT non-parametrically by contrasting pre- and post-treatment weighted Nelson-Aalen survival curves. A closed-form variance is proposed and shown to work well in simulation studies. The proposed methods are applied to national organ transplant registry data.
在评估一种治疗对生存的益处时，将治疗后的生存情况与在未接受治疗时本可观察到的生存函数进行比较通常是很有意义的。在许多实际情况中，治疗是时间依赖性的，也就是说，受试者通常在未接受治疗的情况下开始随访，其中一些人会在随后的某个时间点接受治疗。在观察性研究中，治疗不是随机分配的，因此可能取决于各种患者特征。我们开发了半参数匹配方法，以估计治疗对已治疗者（ATT）在生存概率和受限平均生存时间方面的平均治疗效果。匹配基于一个预后评分，该评分反映了每个患者在未接受治疗时的死亡风险。具体而言，每个接受治疗的患者与多个具有相似预后评分的尚未接受治疗的患者进行匹配。匹配集的大小不需要相等，因为每个匹配的对照都经过加权，以保持治疗组和未治疗组之间的风险评分平衡。匹配后，我们通过对比治疗前和治疗后加权的纳尔逊 - 亚alen生存曲线，非参数地估计ATT。我们提出了一种封闭形式的方差，并证明其在模拟研究中效果良好。所提出的方法应用于国家器官移植登记数据。
