Current models used to predict response to peginterferon plus ribavirin treatment, based on viral decline during the first 12 weeks of therapy, have focused on creating an early stopping rule to avoid unnecessary prolongation of therapy. We developed a multivariate model that predicted sustained virological response and nonresponse at baseline and during the first 12 weeks of therapy using collected data from 186 unselected patients with chronic hepatitis C treated with peginterferon plus ribavirin. This model employed ordinal regression with similarity least squares technology to assign the probability of a given outcome. Model variables include sex, age, prior treatment status, genotype, baseline serum alanine aminotransferase levels, histologic necroinflammation and fibrosis scores and serum hepatitis C virus RNA concentration at baseline and weeks, 4, 8, and 12. A multivariate model demonstrated high performance values at all time points. At baseline, the model demonstrated a negative predictive value (NPV) and a positive predictive value (PPV) of 91% and 95%, respectively. At week 4, these values improved to 97% and 100%, respectively, with 95% sensitivity, 89% specificity and 93% accuracy. At week 4, the model was equally efficient for naïve or previously treated patients. Internal validation demonstrated 90% PPV, 94% NPV, 95% sensitivity, 88% specificity and 92% accuracy. A week 4 stopping rule for patients with chronic hepatitis C treated with peginterferon with ribavirin might be proposed by using the model developed in our study.