Cho Youngjoo, Ghosh Debashis
Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53205.
Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Commun Stat Theory Methods. 2021;50(1):216-236. doi: 10.1080/03610926.2019.1634208. Epub 2019 Jul 15.
Dependent censoring is common in many medical studies, especially when there are multiple occurrences of the event of interest. Ghosh and Lin (2003) and Hsieh, Ding and Wang (2011) proposed estimation procedures using an artificial censoring technique. However, if covariates are not bounded, then these methods can cause excessive artificial censoring. In this paper, we propose estimation procedures for the treatment effect based on a novel application of propensity scores. Simulation studies show that the proposed method provides good finite-sample properties. The techniques are illustrated with an application to an HIV dataset.
在许多医学研究中，相依删失很常见，尤其是当感兴趣的事件有多次发生时。戈什和林（2003年）以及谢、丁和王（2011年）提出了使用人工删失技术的估计程序。然而，如果协变量无界，那么这些方法可能会导致过多的人工删失。在本文中，我们基于倾向得分的一种新应用提出了治疗效果的估计程序。模拟研究表明，所提出的方法具有良好的有限样本性质。通过将这些技术应用于一个艾滋病毒数据集进行了说明。
