Challenges and Opportunities for Use of Cost-Effectiveness Analysis

Abstract

Since the mid-1990s, a number of new, targeted therapies, including cetuximab, have been introduced to treat metastatic colorectal cancer. Despite being dramatically more expensive than older regimens, the targeted therapies produce relatively modest survival benefits and typically improve median survival by no more than several months ( 1 – 3 ). Recently, it has been recognized that patients with tumors that bear a wild type KRAS gene are responsive to cetuximab, whereas patients with tumors bearing KRAS mutations are not ( 4 ). Following this recognition of the predictive value of tumor KRAS mutation status, several practice guidelines began recommending KRAS mutation testing to identify patients for whom cetuximab treatment would not be associated with any clinical benefit ( 5 , 6 ). This practice has rapidly become standard. Restricting cetuximab to colorectal cancer patients with wild-type KRAS tumors has been heralded as a step toward the realization of personalized cancer therapy ( 7 ). Such individually tailored treatment strategies can potentially help address the problems of expensive treatment by limiting their administration to the patients who are most likely to benefit, potentially leading to a more favorable cost-effectiveness of treatment. In this issue of the Journal, Mittmann et al. ( 8 ) reported the cost-effectiveness of cetuximab monotherapy in advanced colorectal cancer patients who participated in the National Cancer Institute of Canada trial CO.17. They found that even when cetuximab therapy is restricted to patients with wild-type KRAS tumors, the incremental cost-effectiveness ratio (ICER) and cost – utility ratio (in 2007 Canadian dollars) were $120 061 per life-year gained and $186 761 per quality-adjusted life-year (QALY) gained, respectively. For the entire study population, the ICER and cost – utility ratio were $199 742 and $299 613, respectively. As expected, based on differential response to cetuximab by KRAS mutation status, the cost per life-year and cost per QALY were more favorable when the analysis was restricted to the subset of patients with wild-type KRAS tumors. Importantly, these estimates do not include the cost of KRAS mutation testing because patients in both arms of the trial received testing and including it in the costs would not have affected the incremental difference. Extrapolation of these trial-based estimates to the general population would need to consider the cost of KRAS mutation testing and would likely lead to even higher ICERs for cetuximab therapy outside a trial setting. These fi ndings raise important questions about the use of cost-effectiveness analysis in policy making. In a setting with unlimited resources, all effective therapies — even those with modest survival benefi ts — could be offered to every patient. But, because in most settings, resources are limited, publicly funded health-care programs, health plans, and other policy makers must weigh the costs and benefi ts of different treatments and make decisions about which treatments will be covered and under what circumstances. The rationale for any cost-effectiveness analysis is that it offers an explicit and transparent approach to quantify the costs and benefi ts of a prevention or treatment strategy by using a common denominator (ie, years of life saved, QALYs saved). The resulting cost-effectiveness ratios can then be compared across conditions with each other or with a threshold value, with the goal of identifying the most effi cient ways of maximizing health at the population level ( 9 ). This approach has the potential advantage of facilitating a deliberative, systematic, and data-driven decision-making process for the allocation of public resources.