Truncated negative binomial mixed regression modelling of ischaemic stroke hospitalizations

A zero-truncated negative binomial mixed regression model is presented to analyse overdispersed positive count data. The study is motivated by the determination of pertinent risk factors associated with ischaemic stroke hospitalizations. Random effects are incorporated in the linear predictor to adjust for inter-hospital variations and the dependency of clustered observations using the generalized linear mixed model approach. The method assists hospital administrators and clinicians to estimate the number of subsequent readmissions based on characteristics of the patient at the index stroke. The findings have important implications on resource usage, rehabilitation planning and management of acute stroke care.

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