Cost Effectiveness of Alternative Strategies for Incorporating Bevacizumab Into the Primary Treatment of Ovarian Cancer

BACKGROUNDThe objective of this study was to evaluate the comparative effectiveness of strategies that incorporated bevacizumab into the primary platinum-based treatment of ovarian cancer: 1) no bevacizumab; 2) concurrent and maintenance bevacizumab for all; 3) bevacizumab for suboptimally debulked stage III and stage IV disease (high-risk cohort); and the evaluation of an alternative exploratory strategy of 4) directed bevacizumab therapy based on a predictive test for bevacizumab responsiveness. METHODSA modified Markov state transition model with a 3-year time horizon that simulated publically available International Collaboration on Ovarian Neoplasms (ICON7) trial outcomes was used to evaluate the cost effectiveness of each strategy. Costs and adverse events were incorporated. An alternative strategy was used to model the impact on overall survival of a genetic-based predictive test. A Monte Carlo simulation simultaneously accounted for uncertainty in key parameters. RESULTSThe incorporation of bevacizumab for high-risk patients had an incremental cost-effectiveness ratio of $168,000 per quality-adjusted life-year (QALY) saved compared with chemotherapy alone and dominated a strategy of giving bevacizumab to all patients with ovarian cancer. Monte Carlo simulation acceptability curves indicated that, at a willingness-to-pay threshold of $200,000 per QALY, the treatment of high-risk women with bevacizumab was the strategy of choice in 84% of simulations. A predictive test had an incremental cost-effectiveness ratio of $129,000 per QALY compared with chemotherapy alone and dominated other bevacizumab treatment strategies. CONCLUSIONSThe selective treatment of women with suboptimal and/or stage IV ovarian cancer was a more cost-effective use of bevacizumab than universal treatment but still did not fall within the limits of common willingness-to-pay thresholds. Continued investigation of potentially cost-effective strategies, such as a predictive test, is necessary to optimize the use of this expensive treatment. Cancer 2013;119:3653-3661. (c) 2013 American Cancer Society.