Chen Chyong-Mei, Lu Tai-Fang C, Chen Man-Hua, Hsu Chao-Min
Department of Statistics and Informatics Science, Providence University, Taichung 43301, Taiwan, R.O.C.
Biom J. 2012 Sep;54(5):641-56. doi: 10.1002/bimj.201100131. Epub 2012 Aug 7.
Current status data arise due to only one feasible examination such that the failure time of interest occurs before or after the examination time. If the examination time is intrinsically related to the failure time of interest, the examination time is referred to as an informative censoring time. Such data may occur in many fields, for example, epidemiological surveys and animal carcinogenicity experiments. To avoid severely misleading inferences resulted from ignoring informative censoring, we propose a class of semiparametric transformation models with log-normal frailty for current status data with informative censoring. A shared frailty is used to account for the correlation between the failure time and censoring time. The expectation-maximization (EM) algorithm combining a sieve method for approximating an infinite-dimensional parameter is employed to estimate all parameters. To investigate finite sample properties of the proposed method, simulation studies are conducted, and a data set from a rodent tumorigenicity experiment is analyzed for illustrative purposes.
当前状态数据仅由一种可行的检查产生，使得感兴趣的失效时间发生在检查时间之前或之后。如果检查时间与感兴趣的失效时间存在内在关联，则该检查时间被称为信息性删失时间。此类数据可能出现在许多领域，例如流行病学调查和动物致癌性实验中。为避免因忽略信息性删失而导致严重误导性的推断，我们针对具有信息性删失的当前状态数据，提出了一类具有对数正态脆弱性的半参数变换模型。使用共享脆弱性来考虑失效时间和删失时间之间的相关性。采用结合用于近似无限维参数的筛分法的期望最大化（EM）算法来估计所有参数。为研究所提方法的有限样本性质，进行了模拟研究，并分析了来自啮齿动物致瘤性实验的一个数据集以作说明。
