ArticleBioMed research international2013
Application of zero-inflated poisson mixed models in prognostic factors of hepatitis C.
Article in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Influencing factors of multiple adverse outcomes among schizophrenia patients using count regression models: a cross-sectional study.BMC psychiatry · 2022Article
- Bayesian Zero- Inflated Poisson model for prognosis of demographic factors associated with using crystal meth in Tehran population.Medical journal of the Islamic Republic of Iran · 2018Article
- Application of hurdle model with random effects for evaluating the balance improvement in stroke patients.Medical journal of the Islamic Republic of Iran · 2015Article
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Authors and funding
5 authors.
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Abstract
BACKGROUND AND
objectivesIn recent years, hepatitis C virus (HCV) infection represents a major public health problem. Evaluation of risk factors is one of the solutions which help protect people from the infection. This study aims to employ zero-inflated Poisson mixed models to evaluate prognostic factors of hepatitis C.
methodsThe data was collected from a longitudinal study during 2005-2010. First, mixed Poisson regression (PR) model was fitted to the data. Then, a mixed zero-inflated Poisson model was fitted with compound Poisson random effects. For evaluating the performance of the proposed mixed model, standard errors of estimators were compared.
resultsThe results obtained from mixed PR showed that genotype 3 and treatment protocol were statistically significant. Results of zero-inflated Poisson mixed model showed that age, sex, genotypes 2 and 3, the treatment protocol, and having risk factors had significant effects on viral load of HCV patients. Of these two models, the estimators of zero-inflated Poisson mixed model had the minimum standard errors.
conclusionsThe results showed that a mixed zero-inflated Poisson model was the almost best fit. The proposed model can capture serial dependence, additional overdispersion, and excess zeros in the longitudinal count data.
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