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BioMed Research International
Volume 2013 (2013), Article ID 403151, 5 pages
http://dx.doi.org/10.1155/2013/403151
Research Article

Application of Zero-Inflated Poisson Mixed Models in Prognostic Factors of Hepatitis C

1Department of Basic Sciences, School of Rehabilitation, Shahid Beheshti University of Medical Science, Tehran, Iran
2Department of Biostatistics, School of Paramedical Science, Shahid Beheshti University of Medical Science, Tehran, Iran
3Proteomics Research Center, School of Paramedical Science, Shahid Beheshti University of Medical Science, Tehran, Iran
4Department of Statistics, Yazd University, Yazd, Iran
5Baqiyatallah Research Center for Gastroenterology and Liver Diseases, Baqiyatallah University of Medical Sciences, Tehran, Iran

Received 9 April 2013; Revised 25 August 2013; Accepted 28 August 2013

Academic Editor: Roya Kelishadi

Copyright © 2013 Alireza Akbarzadeh Baghban et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background and Objectives. In 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. Methods. The 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. Results. The 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. Conclusions. The 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.