OBJECTIVES: Our objective was to evaluate the cost-effectiveness of currently recommended pharmacotherapies [response-guided triple therapies with pegylated-interferon plus ribavarin (PR) plus boceprevir (BOC) or telaprevir (TVR) or simeprevir (SIM) or sofosbuvir (SOF)]  for the treatment of newly diagnosed patients with genotype 1 chronic hepatitis C virus (HCV) infection, from payer’s perspective. METHODS: A Markov cohort model was developed to simulate the disease progression of treatment naïve HCV patients initiating therapy. The model included the following outcomes: sustained virologic response (SVR) i.e. absence of detectable RNA of virus in blood serum at 12/24 weeks after ending treatment, relapse from remission, progression to long-term adverse outcomes (cirrhosis, decompensated cirrhosis, hepatocellular carcinoma, end-stage liver disease) and commonly observed side effects (anemia, neutropenia and rash) along with a gradient of their severity. Model inputs were determined from a review of the published literature and phase II or III clinical trials. Clinical trials estimates were converted to risk ratios to facilitate comparison. Costs were inflated to 2013 US dollars. Costs and quality adjusted life years were discounted at standard rate of 5%. Sensitivity analyses were performed to assess model sensitivity and address uncertainty. RESULTS: Relative to treatment with PR only, response-guided triple therapy with BOC+PR was the most cost effective therapy (ICER $28,723/QALY) followed by response-guided triple therapy with TVR+PR (ICER $64,569/QALY). PR treatments with SIM or SOF were dominated by BOC+PR. The model was sensitive to variations in costs of initial drug therapy and likelihood of attaining SVR.   CONCLUSIONS: Addition of protease inhibitors to PR therapy improves health outcomes. Response-guided regimen BOC+PR was found to be cost-effective for treating newly diagnosed genotype 1 HCV patients. Shortening of PR therapy guided by a rapid virologic response may help reduce overall costs. Robust sensitivity analyses can help in overcoming the challenge of sparse data availability.