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A joint model for hierarchical continuous and zero-inflated overdispersed count data

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dc.contributor.author Wondwosen Kassahun
dc.contributor.author Thomas Neyens
dc.contributor.author Geert Molenberghs
dc.date.accessioned 2020-12-11T08:50:05Z
dc.date.available 2020-12-11T08:50:05Z
dc.date.issued 2013
dc.identifier.uri http://10.140.5.162//handle/123456789/2962
dc.description.abstract Many applications in public health, medical and biomedical or other studies demand modelling of two or more longitudinal outcomes jointly to get better insight into their joint evolution. In this regard, a jointmodel for a longitudinal continuous and a count sequence, the latter possibly overdispersed and zero-inflated (ZI), will be specified that assembles aspects coming from each one of them into one single model. Further, a subject-specific random effect is included to account for the correlation in the continuous outcome. For the count outcome, clustering and overdispersion are accommodated through two distinct sets of random effects in a generalized linear model as proposed by Molenberghs et al. [A family of generalized linear models for repeated measures with normal and conjugate random effects. Stat Sci. 2010;25:325–347]; one is normally distributed, the other conjugate to the outcome distribution. The association among the two sequences is captured by correlating the normal random effects describing the continuous and count outcome sequences, respectively. An excessive number of zero counts is often accounted for by using a so-called ZI or hurdle model. ZI models combine either a Poisson or negative-binomial model with an atom at zero as a mixture, while the hurdle model separately handles the zero observations and the positive counts. This paper proposes a general joint modelling framework in which all these features can appear together. We illustrate the proposed method with a case study and examine it further with simulations. en_US
dc.language.iso en en_US
dc.subject clustering en_US
dc.subject conjugate random effect en_US
dc.subject hurdle model en_US
dc.subject joint mode en_US
dc.subject normal random effect en_US
dc.subject overdispersion en_US
dc.subject zero-inflatio en_US
dc.title A joint model for hierarchical continuous and zero-inflated overdispersed count data en_US
dc.type Article en_US


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