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Semiparametric additive marginal regression models for multiple type recurrent events

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Abstract

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.

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Authors and Affiliations

  1. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Xiaolin Chen & Qihua Wang

  2. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7420, USA

    Jianwen Cai & Viswanathan Shankar

Authors
  1. Xiaolin Chen
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  2. Qihua Wang
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  3. Jianwen Cai
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  4. Viswanathan Shankar
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Corresponding author

Correspondence to Qihua Wang.

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Chen, X., Wang, Q., Cai, J. et al. Semiparametric additive marginal regression models for multiple type recurrent events. Lifetime Data Anal 18, 504–527 (2012). https://doi.org/10.1007/s10985-012-9226-4

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  • DOI: https://doi.org/10.1007/s10985-012-9226-4

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