Semiparametric additive marginal regression models for multiple type recurrent events.
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.
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, People's Republic of China.
Xiaolin
X
Chen
Qihua
Q
Wang
Jianwen
J
Cai
Viswanathan
V
Shankar
D035843:Biomedical Research / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007194:India / Q000453:epidemiology; D007239:Infections / Q000453:epidemiology / Q000209:etiology; D016030:Kidney Transplantation / Q000009:adverse effects / Q000706:statistics & numerical data; D015233:Models, Statistical; D012008:Recurrence; D012044:Regression Analysis