Using statistical models to assess medical cost of hepatitis C virus

AIM: This study compared PR and NB in predicting HCV patient costs. The objective of this study was to predict the direct cost of the HCV patient in Iran. BACKGROUND: Hepatitis C virus (HCV) is a common and expensive infectious disease in Iran. Cost associated with HCV and its complications has not been well characterized. Analysis of cost data is important in providing consistent information to aid budgeting decisions and certain statistical regression models need for prediction mean costs. Poisson regression (PR) and negative binomial regression (NB) are more common in cost prediction study. PATIENTS AND METHODS: This study designed as a cross-sectional clinic base from 2001 to 2010. First treatment period of each patient bring in study. We evaluated the doctor visiting, drugs, and hospitalization and laboratory tests of patients. Cost per person per one treatment period estimated in purchasing power parity dollars (PPP$). The PR is one of the models from general linear models (GLM) for describing count outcomes. The NB is another model from (GLM) as an alternative to the PR model. RESULTS: According to Likelihood ratio test NB was found to be more appropriate than PR (P < 0.001). Genotype, marriage, medication, and SVR were being significant. Genotype 3 versus 1 decreasing cost while marriage, consuming pegasys and SVR increasing. CONCLUSION: Choosing best model in cost data is important because of specific feature of this data. After fitting the best model, analyzing and predicting future cost for patient in different situation is possible.