Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17436
Title: An additive-multiplicative rates model for multivariate recurrent events with event categories missing at random
Authors: Ye, P
Sun, L
Zhao, X 
Xu, W
Keywords: Additive-multiplicative rates model
Missing data
Multivariate recurrent events
Semiparametric model
Weighted estimating equation
Issue Date: 2015
Publisher: Science in China Press
Source: Science China. Mathematics, 2015 How to cite?
Journal: Science China. Mathematics 
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 article, 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.
URI: http://hdl.handle.net/10397/17436
ISSN: 1674-7283
DOI: 10.1007/s11425-015-5000-x
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