Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61063
Title: A semiparametric additive rates model for multivariate recurrent events with missing event categories
Authors: Ye, P
Zhao, X 
Sun, L
Xu, W
Keywords: Additive rates model
Marginal models
Missing at random
Multivariate recurrent events
Weighted estimating equation
Issue Date: 2015
Publisher: North-Holland
Source: Computational statistics and data analysis, 2015, v. 89, p. 39-50 How to cite?
Journal: Computational statistics and data analysis 
Abstract: 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.
URI: http://hdl.handle.net/10397/61063
ISSN: 0167-9473
DOI: 10.1016/j.csda.2015.03.002
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