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A semiparametric additive rates model for clustered recurrent event data

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Abstract

Recurrent event data often arises in biomedical studies, and individuals within a cluster might not be independent. We propose a semiparametric additive rates model for clustered recurrent event data, wherein the covariates are assumed to add to the unspecified baseline rate. For the inference on the model parameters, estimating equation approaches are developed, and both large and finite sample properties of the proposed estimators are established.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Central China Normal University, Wuhan, 430079, China

    Sui He & Fen Wang

  2. Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Liu-quan Sun

Authors
  1. Sui He
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  2. Fen Wang
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  3. Liu-quan Sun
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Corresponding author

Correspondence to Liu-quan Sun.

Additional information

This research was supported by International Cooperation Projects (2010DFA31790) of Chinese Ministry of Science and Technology and the fund of Central China Normal University for Ph.D students (No. 2009023). The third author’s research was partly supported by the National Natural Science Foundation of China Grants (No. 10731010, 10971015 and 11021161), the National Basic Research Program of China (973 Program) (No. 2007CB814902) and Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics & Systems Science, Chinese Academy of Sciences (No. 2008DP173182).

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He, S., Wang, F. & Sun, Lq. A semiparametric additive rates model for clustered recurrent event data. Acta Math. Appl. Sin. Engl. Ser. 29, 55–62 (2013). https://doi.org/10.1007/s10255-011-0093-7

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  • DOI: https://doi.org/10.1007/s10255-011-0093-7

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