Biostatistics Center, China Medical University, Taichung, Taiwan.
Stat Med. 2013 Feb 28;32(5):822-32. doi: 10.1002/sim.5562. Epub 2012 Aug 22.
Methods for analyzing interval-censored data are well established. Unfortunately, these methods are inappropriate for the studies with correlated data. In this paper, we focus on developing a method for analyzing clustered interval-censored data. Our method is based on Cox's proportional hazard model with piecewise-constant baseline hazard function. The correlation structure of the data can be modeled by using Clayton's copula or independence model with proper adjustment in the covariance estimation. We establish estimating equations for the regression parameters and baseline hazards (and a parameter in copula) simultaneously. Simulation results confirm that the point estimators follow a multivariate normal distribution, and our proposed variance estimations are reliable. In particular, we found that the approach with independence model worked well even when the true correlation model was derived from Clayton's copula. We applied our method to a family-based cohort study of pandemic H1N1 influenza in Taiwan during 2009-2010. Using the proposed method, we investigate the impact of vaccination and family contacts on the incidence of pH1N1 influenza.
分析区间删失数据的方法已经很成熟。不幸的是，这些方法不适用于具有相关性数据的研究。在本文中，我们专注于开发一种分析聚类区间删失数据的方法。我们的方法基于具有分段常数基线风险函数的 Cox 比例风险模型。可以通过使用 Clayton 连接函数或具有适当协方差估计调整的独立性模型来对数据的相关结构进行建模。我们同时为回归参数和基线风险（和连接函数中的一个参数）建立估计方程。模拟结果证实，点估计值遵循多元正态分布，并且我们提出的方差估计是可靠的。特别是，我们发现即使真实相关模型来自 Clayton 连接函数，独立性模型的方法也能很好地工作。我们将我们的方法应用于 2009-2010 年台湾发生的大流行性 H1N1 流感的基于家庭的队列研究。使用所提出的方法，我们研究了接种疫苗和家庭接触对 pH1N1 流感发病率的影响。
