A transformation approach for the analysis of interval-censored failure time data
A transformation approach for the analysis of interval-censored failure time data
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DOI：
10.1007/s10985-007-9075-8
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发表时间：
2008-06-01
影响因子：
1.3
通讯作者：
Sun, Jianguo
中科院分区：
文献类型：
--
作者：
Zhu, Liang;Tong, Xingwei;Sun, Jianguo
本文讨论了近年来备受关注的失效时间间隔截短数据的分析(Li and Pu, Lifetime data Anal 9:57-70, 2003; Sun， interval-截短数据的统计分析，2006;Tian and Cai, Biometrika 93(2):329-342, 2006；[J] .科学通报，2005(33):61-70。间隔删减数据意味着观察到的感兴趣的生存时间只属于一个间隔，它们出现在许多领域，包括临床试验、人口研究、医学后续研究、公共卫生研究和致瘤性实验。分析间隔审查数据的一个主要困难是必须处理涉及两个相关变量的审查机制。在推理方面，我们提出了一种将一般间隔截尾数据转换为当前状态数据的转换方法，只需处理一个截尾变量，推理就容易了。我们将这一一般思想应用于区间截后数据的加性风险模型的回归分析，数值研究表明，该方法在实际情况下表现良好。给出了一个说明性示例。
This paper discusses the analysis of interval-censored failure time data, which has recently attracted a great amount of attention (Li and Pu, Lifetime Data Anal 9:57-70, 2003; Sun, The statistical analysis of interval-censored data, 2006; Tian and Cai, Biometrika 93(2):329-342, 2006; Zhang et al., Can J Stat 33:61-70, 2005). Interval-censored data mean that the survival time of interest is observed only to belong to an interval and they occur in many fields including clinical trials, demographical studies, medical follow-up studies, public health studies and tumorgenicity experiments. A major difficulty with the analysis of interval-censored data is that one has to deal with a censoring mechanism that involves two related variables. For the inference, we present a transformation approach that transforms general interval-censored data into current status data, for which one only needs to deal with one censoring variable and the inference is thus much easy. We apply this general idea to regression analysis of interval-censored data using the additive hazards model and numerical studies indicate that the method performs well for practical situations. An illustrative example is provided.