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

Peng Ye, Liu Quan Sun, Xingqiu Zhao, Wei Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1163-1178
Number of pages16
JournalScience China Mathematics
Issue number6
Publication statusPublished - 1 Jun 2015


  • additive-multiplicative rates model
  • missing data
  • multivariate recurrent events
  • semiparametric model
  • weighted estimating equation

ASJC Scopus subject areas

  • Mathematics(all)

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