A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use

By Brian H Neelon, A. O’Malley, Sharon-Lise T. Normand. 2010

144 citations recorded by the source corpus.

In applications involving count data, it is common to encounter an excess number of zeros. For example, in the study of outpatient service utilization, the number of utilization days will take on integer values, with many subjects having no utilization (zero values). Mixed distribution models, such as the zero-inflated Poisson and zero-inflated negative binomial, are often used to fit such data. A more general class of mixture models, called hurdle models, can be used to model zero deflation as well as zero inflation. Several authors have proposed frequentist approaches to fitting zero-inflated models for repeated measures. We describe a practical Bayesian approach which incorporates prior information, has optimal small-sample properties and allows for tractable inference. The approach can be easily implemented using standard Bayesian software. A study of psychiatric outpatient service use illustrates the methods.

Subfields: Artificial Intelligence, Statistics and Probability, Probabilistic & Bayesian Methods, Medical & Health AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Noriszura Ismail
2. Hossein Zamani
3. S. Aryuyuen
4. Winai Bodhisuwan

Citations in the corpus

• Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Bayesian models have been shown to lead to great insights in assorted applications such as cognitive modeling [201,202] , fishery [196,203] , gaming [204] , climate [205] , economet-rics [206] or robotics [207] .”
• Fire severity and landscape context effects on arboreal marsupials (2013, unknown): “The code used was 287 described in Neelon et al. (2010).”
• Estimation of Claim Count Data using Negative Binomial, Generalized Poisson, Zero-Inflated Negative Binomial and Zero-Inflated Generalized Poisson Regression Models (2013, background): “In healthcare area as another example, services of psychiatric outpatient may report a large proportion of zero utilization of such services for many patients (Neelon et al. 2010).”