A cost-benefit analysis of bevacizumab in combination with paclitaxel in the first-line treatment of patients with metastatic breast cancer

Abstract

Bevacizumab in combination with chemotherapy increases progression-free survival (PFS), but not overall survival when compared to chemotherapy alone in the treatment of metastatic breast cancer (MBC). Recently in November, 2011 the Food and drug administration revoked approval of bevacizumab in combination with paclitaxel for the treatment of MBC. The European Medicines Agency, in contrast, maintained its approval of bevacizumab in MBC. While neither agency considers health economics in their decision-making process, one of the greatest challenges in oncology practice today is to reconcile hard-won small incremental clinical benefits with exponentially rising costs. To inform policy-makers in the US, this study aimed to assess the cost-effectiveness of bevacizumab/paclitaxel in MBC, from a payer perspective. We created a decision analytical model using efficacy and adverse events data from the ECOG 2100 trial. Health utilities were derived from available literature. Costs were obtained from the Center for Medicare Services Drug Payment Table and Physician Fee Schedule and are represented in 2010 US dollars. Quality-adjusted life-years (QALY) and incremental cost-effectiveness ratio (ICER) were calculated. Sensitivity analyses were performed. Bevacizumab added 0.49 years of PFS and 0.135 QALY with an incremental cost of $100,300, and therefore a cost of $204,000 per year of PFS gained and an ICER of $745,000 per QALY. The main drivers of the model were drug acquisition cost, PFS, and health utility values. Using a threshold of $150,000/QALY, drug price would have to be reduced by nearly 80% or alternatively PFS increased by 10 months to make bevacizumab cost-effective. The results of the model were robust in sensitivity analyses. Bevacizumab plus paclitaxel is not cost-effective in treating MBC. Value-based pricing and the development of biomarkers to improve patient selection are needed to better define the role of the drug in this population.