Statistical count models for prognosis the risk factors of hepatitis C

Aim: The aim of this study was to compare alternatives methods for analysis of zero inflated count data and compare them with simple count models that are used by researchers frequently for such zero inflated data. Background: Analysis of viral load and risk factors could predict likelihood of achieving sustain virological response (SVR). This information is useful to protect a person from acquiring Hepatitis C virus (HCV) infection. The distribution of viral load contains a large proportion of excess zeros (HCV-RNA under 100), that can lead to over-dispersion. Patients and methods: This data belonged to a longitudinal study conducted between 2005 and 2010. The response variable was the viral load of each HCV patient 6 months after the end of treatment. Poisson regression (PR), negative binomial regression (NB), zero inflated Poisson regression (ZIP) and zero inflated negative binomial regression (ZINB) models were carried out to the data respectively. Log likelihood, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to compare performance of the models. Results: According to all criterions, ZINB was the best model for analyzing this data. Age, having risk factors genotype 3 and protocol of treatment were being significant. Conclusion: Zero inflated negative binomial regression models fit the viral load data better than the Poisson, negative binomial and zero inflated Poisson models.