Chen Xiaolin, Wang Qihua, Cai Jianwen, Shankar Viswanathan
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, People's Republic of China.
Lifetime Data Anal. 2012 Oct;18(4):504-27. doi: 10.1007/s10985-012-9226-4. Epub 2012 Aug 17.
Recurrent event data are often encountered in biomedical research, for example, recurrent infections or recurrent hospitalizations for patients after renal transplant. In many studies, there are more than one type of events of interest. Cai and Schaube (Lifetime Data Anal 10:121-138, 2004) advocated a proportional marginal rate model for multiple type recurrent event data. In this paper, we propose a general additive marginal rate regression model. Estimating equations approach is used to obtain the estimators of regression coefficients and baseline rate function. We prove the consistency and asymptotic normality of the proposed estimators. The finite sample properties of our estimators are demonstrated by simulations. The proposed methods are applied to the India renal transplant study to examine risk factors for bacterial, fungal and viral infections.
复发事件数据在生物医学研究中经常遇到，例如，肾移植后患者的反复感染或反复住院。在许多研究中，存在不止一种类型的感兴趣事件。蔡和绍布（《生存数据分析》10:121 - 138，2004年）针对多种类型的复发事件数据提出了一种比例边际率模型。在本文中，我们提出了一种一般加性边际率回归模型。采用估计方程法来获得回归系数和基线率函数的估计量。我们证明了所提出估计量的一致性和渐近正态性。通过模拟展示了我们估计量的有限样本性质。所提出的方法应用于印度肾移植研究，以检查细菌、真菌和病毒感染的风险因素。
