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Lifetime Data Anal (2010) 16:353373
DOI 10.1007/s10985-010-9160-2
Received: 20 November 2008 / Accepted: 26 February 2010 / Published online: 14 March 2010 Springer Science+Business Media, LLC 2010
Abstract Recurrent events are frequently encountered in biomedical studies. Evaluating the covariates effects on the marginal recurrent event rate is of practical interest. There are mainly two types of rate models for the recurrent event data: the multiplicative rates model and the additive rates model. We consider a more exible additivemultiplicative rates model for analysis of recurrent event data, wherein some covariate effects are additive while others are multiplicative. We formulate estimating equations for estimating the regression parameters. The estimators for these regression parameters are shown to be consistent and asymptotically normally distributed under appropriate regularity conditions. Moreover, the estimator of the baseline mean function is proposed and its large sample properties are investigated. We also conduct simulation studies to evaluate the nite sample behavior of the proposed estimators. A medical study of patients with cystic brosis suffered from recurrent pulmonary exacerbations is provided for illustration of the proposed method.
Keywords Recurrent events Rate regression Additivemultiplicative rates
model Counting process Empirical process
Y. Liu Y. Wu
School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei 430072, China
Y. Wu J. Cai (B) H. Zhou
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420, USAe-mail: [email protected]
Additivemultiplicative rates model for recurrent events
Yanyan Liu Yuanshan Wu Jianwen Cai
Haibo Zhou
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354 Y. Liu et al.
1 Introduction
Recurrent event data are common in biomedical studies. For example, patients with cystic brosis may suffer from repeated pulmonary exacerbations of respiratory symptoms (Therneau and Grambsch 2000); HIV patients may experience recurrent opportunistic infections (Li and Lagakos 1997). Other examples include myocardial infarctions, tumor metastases etc.
Modeling the occurrence of recurrent events has been a much discussed topic in the last few years and recurrent event data can be viewed as a special case of multivariate failure time data since the different event times within the same subject are ordered and thus correlated. Therefore, these data can be analyzed by well-established marginal intensity model approaches (e.g., Wei et al. 1989; Lee et al. 1992) and conditional intensity model approaches (e.g., Prentice et al. 1981; Andersen...