Lee, JooyoungCook, Richard J.2021-01-132021-01-132019-09-20https://doi.org/10.1002/sim.8283http://hdl.handle.net/10012/16636This is the peer reviewed version of the following article: Jooyoung Lee and Richard J. Cook, Dependence modeling for multi-type recurrent events via copulas, Statistics in Medicine (2019), 38(21): 4066–4082 which has been published in final form at https://doi.org/10.1002/sim.8283.This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.When several types of recurrent events may arise, interest often lies in marginal modeling and studying the nature of the dependence structure. In this paper, we propose a multivariate mixed-Poisson model with the dependence between events accommodated by type-specific random effects which are associated through use of a Gaussian copula. Such models retain marginal features with a simple interpretation, reflect the heterogeneity in risk for each type of event, and provide insight into the dependence between the different types of events. Semiparametric inference is proposed based on composite likelihood to avoid high dimensional integration. An application to a study of nutritional supplements in malnourished children is given in which the goal is to evaluate the reduction in the rate of several different kinds of infection.encomposite likelihoodcopulaEM algorithmfrailtymulti-type recurrent eventsArticle