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An additive-multiplicative rates model for multivariate recurrent events with event categories missing at random

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Abstract

Multivariate recurrent event data arises when study subjects may experience more than one type of recurrent events. In some situations, however, although event times are always observed, event categories may be partially missing. In this paper, an additive-multiplicative rates model is proposed for the analysis of multivariate recurrent event data when event categories are missing at random. A weighted estimating equations approach is developed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. In addition, a model-checking technique is presented to assess the adequacy of the model. Simulation studies are conducted to evaluate the finite sample behavior of the proposed estimators, and an application to a platelet transfusion reaction study is provided.

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Authors and Affiliations

  1. Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100080, China

    Peng Ye & LiuQuan Sun

  2. Department of Applied Mathematics, The Hong Kong Polytechnic University Shenzhen Research Institute, The Hong Kong Polytechnic University, Hong Kong, China

    XingQiu Zhao

  3. Dalla Lana School of Public Health, University of Toronto, Toronto, Canada

    Wei Xu

Authors
  1. Peng Ye
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  2. LiuQuan Sun
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  3. XingQiu Zhao
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  4. Wei Xu
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Correspondence to Peng Ye.

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Ye, P., Sun, L., Zhao, X. et al. An additive-multiplicative rates model for multivariate recurrent events with event categories missing at random. Sci. China Math. 58, 1163–1178 (2015). https://doi.org/10.1007/s11425-015-5000-x

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  • DOI: https://doi.org/10.1007/s11425-015-5000-x

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