Li Gang, Tseng Chi-Hong
Department of Biostatistics, University of California at Los Angeles.
Scand Stat Theory Appl. 2008 Jun 1;35(2):228-247. doi: 10.1111/j.1467-9469.2007.00581.x.
The two-stage design is popular in epidemiology studies and clinical trials due to its cost effectiveness. Typically, the first stage sample contains cheaper and possibly biased information, while the second stage validation sample consists of a subset of subjects with accurate and complete information. In this paper, we study estimation of a survival function with right-censored survival data from a two-stage design. A non-parametric estimator is derived by combining data from both stages. We also study its large sample properties and derive pointwise and simultaneous confidence intervals for the survival function. The proposed estimator effectively reduces the variance and finite-sample bias of the Kaplan-Meier estimator solely based on the second stage validation sample. Finally, we apply our method to a real data set from a medical device post-marketing surveillance study.
两阶段设计因其成本效益在流行病学研究和临床试验中很受欢迎。通常，第一阶段样本包含成本较低且可能有偏差的信息，而第二阶段验证样本由具有准确和完整信息的受试者子集组成。在本文中，我们研究了来自两阶段设计的右删失生存数据的生存函数估计。通过合并两个阶段的数据得出了一个非参数估计量。我们还研究了它的大样本性质，并得出了生存函数的逐点和同时置信区间。所提出的估计量有效地降低了仅基于第二阶段验证样本的Kaplan-Meier估计量的方差和有限样本偏差。最后，我们将我们的方法应用于一个医疗器械上市后监测研究的真实数据集。
