Bellamy Scarlett L, Li Yi, Ryan Louise M, Lipsitz Stuart, Canner Marina J, Wright Rosalind
Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, 629 Blockley Hall/423 Guardian Drive, Philadelphia, PA 19103, USA.
Stat Med. 2004 Dec 15;23(23):3607-21. doi: 10.1002/sim.1918.
Many authors in recent years have proposed extensions of familiar survival analysis methodologies to apply in dependent data settings, for example, when data are clustered or subject to repeated measures. However, these extensions have been considered largely in the context of right censored data. In this paper, we discuss a parametric frailty model for the analysis of clustered and interval censored failure time data. Details are presented for the specific case where the underlying time to event data follow a Weibull distribution. Maximum likelihood estimates will be obtained using commercially available software and the empirical efficiency of these estimators will be explored via a simulation study. We also discuss a score test to make inferences about the magnitude and significance of over-dispersion in clustered data settings. These methods will be illustrated using data from the East Boston Asthma Study.
近年来，许多作者提出了对常见生存分析方法的扩展，以应用于相关数据设置，例如，当数据聚类或受到重复测量时。然而，这些扩展主要是在右删失数据的背景下进行考虑的。在本文中，我们讨论一种用于分析聚类和区间删失失效时间数据的参数脆弱模型。针对事件发生时间数据服从威布尔分布的具体情况给出了详细内容。将使用商业软件获得最大似然估计，并通过模拟研究探索这些估计量的经验效率。我们还讨论了一种得分检验，以推断聚类数据设置中过度离散的程度和显著性。将使用来自东波士顿哮喘研究的数据来说明这些方法。
