Lu Xiaomin, Tsiatis Anastasios A
Department of Biostatistics, College of Medicine and College of Public Health and health Professions, University of Florida, Gainesville, FL 32611, USA.
Lifetime Data Anal. 2011 Oct;17(4):566-93. doi: 10.1007/s10985-011-9199-8. Epub 2011 Jun 26.
In many randomized clinical trials, the primary response variable, for example, the survival time, is not observed directly after the patients enroll in the study but rather observed after some period of time (lag time). It is often the case that such a response variable is missing for some patients due to censoring that occurs when the study ends before the patient's response is observed or when the patients drop out of the study. It is often assumed that censoring occurs at random which is referred to as noninformative censoring; however, in many cases such an assumption may not be reasonable. If the missing data are not analyzed properly, the estimator or test for the treatment effect may be biased. In this paper, we use semiparametric theory to derive a class of consistent and asymptotically normal estimators for the treatment effect parameter which are applicable when the response variable is right censored. The baseline auxiliary covariates and post-treatment auxiliary covariates, which may be time-dependent, are also considered in our semiparametric model. These auxiliary covariates are used to derive estimators that both account for informative censoring and are more efficient then the estimators which do not consider the auxiliary covariates.
在许多随机临床试验中，主要反应变量，例如生存时间，并非在患者入组研究后直接观测到，而是在一段时间（滞后时间）后观测到。通常会出现这样的情况，即由于在患者反应被观测到之前研究就结束了或者患者退出研究而发生删失，部分患者的这种反应变量会缺失。通常假定删失是随机发生的，这被称为非信息性删失；然而，在许多情况下这种假定可能并不合理。如果缺失数据没有得到恰当分析，治疗效果的估计量或检验可能会有偏差。在本文中，我们使用半参数理论来推导一类用于治疗效果参数的一致且渐近正态的估计量，当反应变量是右删失时这些估计量是适用的。我们的半参数模型中还考虑了可能随时间变化的基线辅助协变量和治疗后辅助协变量。这些辅助协变量用于推导既能考虑信息性删失又比不考虑辅助协变量的估计量更有效的估计量。
