Nie Hui, Cheng Jing, Small Dylan S
Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
Biometrics. 2011 Dec;67(4):1397-405. doi: 10.1111/j.1541-0420.2011.01575.x. Epub 2011 Mar 8.
In many clinical studies with a survival outcome, administrative censoring occurs when follow-up ends at a prespecified date and many subjects are still alive. An additional complication in some trials is that there is noncompliance with the assigned treatment. For this setting, we study the estimation of the causal effect of treatment on survival probability up to a given time point among those subjects who would comply with the assignment to both treatment and control. We first discuss the standard instrumental variable (IV) method for survival outcomes and parametric maximum likelihood methods, and then develop an efficient plug-in nonparametric empirical maximum likelihood estimation (PNEMLE) approach. The PNEMLE method does not make any assumptions on outcome distributions, and makes use of the mixture structure in the data to gain efficiency over the standard IV method. Theoretical results of the PNEMLE are derived and the method is illustrated by an analysis of data from a breast cancer screening trial. From our limited mortality analysis with administrative censoring times 10 years into the follow-up, we find a significant benefit of screening is present after 4 years (at the 5% level) and this persists at 10 years follow-up.
在许多有生存结局的临床研究中，当随访在预定日期结束且许多受试者仍存活时，就会发生行政删失。在一些试验中，另一个复杂情况是存在对分配治疗的不依从性。对于这种情况，我们研究在那些会依从治疗组和对照组分配的受试者中，治疗对直至给定时间点的生存概率的因果效应估计。我们首先讨论生存结局的标准工具变量（IV）方法和参数最大似然方法，然后开发一种有效的插件式非参数经验最大似然估计（PNEMLE）方法。PNEMLE方法不对结局分布做任何假设，并利用数据中的混合结构以比标准IV方法更有效。推导了PNEMLE的理论结果，并通过对一项乳腺癌筛查试验的数据进行分析来说明该方法。从我们在随访10年时进行行政删失时间的有限死亡率分析中，我们发现筛查在4年后（在5%水平）存在显著益处，并且在10年随访时这种益处仍然存在。
