Random effect model for identifying related factors to virological response in HCV patients

Aim This study aims to employ random effect model to evaluate prognostic factors of hepatitis C. Background In recent years, Hepatitis C virus (HCV) infection has been a major cause of liver diseases worldwide and represents a major public health problem. Evaluation of risk factors and a community intervention in order to decrease the problem is one of the solutions which help protect people from the infection. Patients and methods The data was collected from a longitudinal study during 2005-2010. The response variable in this study was the viral load of each HCV patient during the treatment, immediately after the treatment and 3 to 4 months after the end of the treatment. The outcome variable of interest is the viral load of HCV patients. For analyzing repeated measure viral load of HCV patients, random effect models were used. Results The results obtained from random effect model showed that treatment protocol and time statistically significant. The variance component was statistically differing with zero. Conclusion According to the results time had a positive effect on rate of viral load of patient. Combination therapy of Peg-interferon plus Ribavirin increased the rate of virological response.