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A class of mixed models for recurrent event data
Author(s) -
Sun Liuquan,
Zhao Xingqiu,
Zhou Jie
Publication year - 2011
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.10132
Subject(s) - estimator , mathematics , humanities , covariate , statistics , econometrics , philosophy
In this article, we propose a class of mixed models for recurrent event data. The new models include the proportional rates model and Box–Cox transformation rates models as special cases, and allow the effects of covariates on the rate functions of counting processes to be proportional or convergent. For inference on the model parameters, estimating equation approaches are developed. The asymptotic properties of the resulting estimators are established and the finite sample performance of the proposed procedure is evaluated through simulation studies. A real example with data taken from a clinic study on chronic granulomatous disease (CGD) is also illustrated for the use of the proposed methodology. The Canadian Journal of Statistics 39: 578–590; 2011. © 2011 Statistical Society of Canada