A semiparametric additive rates model for multivariate recurrent events with missing event categories

Peng Ye, Xingqiu Zhao, Liuquan Sun, Wei Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


Multivariate recurrent event data arise in many clinical and observational studies, in which subjects may experience multiple types of recurrent events. In some applications, event times can be always observed, but types for some events may be missing. In this article, a semiparametric additive rates model is proposed for analyzing multivariate recurrent event data when event categories are missing at random. A weighted estimating equation approach is developed to estimate parameters of interest, and the resulting estimators are shown to be consistent and asymptotically normal. In addition, a lack-of-fit test is presented to assess the adequacy of the model. Simulation studies demonstrate that the proposed method performs well for practical settings. An application to a platelet transfusion reaction study is provided.
Original languageEnglish
Pages (from-to)39-50
Number of pages12
JournalComputational Statistics and Data Analysis
Publication statusPublished - 1 Sep 2015


  • Additive rates model
  • Marginal models
  • Missing at random
  • Multivariate recurrent events
  • Weighted estimating equation

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics

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