How does your health technology (e.g., drug, vaccine, device, diagnostic) compare to competing alternatives in the same therapeutic area? Is it considered cost-effective? Cost-effectiveness focuses on maximizing health benefits for a given budget. It does not simply indicate the least expensive or the most effective alternative. There are 5 common scenarios where a health technology is deemed cost-effective.
The first three scenarios are straightforward. Compared to the standard of care and other alternatives, your health technology may either (1) cost less with more clinical benefit, (2) cost less with the same benefit, or (3) cost the same with more benefit. The next two scenarios indicate a trade-off, wherein your technology may (4) cost less with less benefit, or (5) cost more with more benefit.
Understanding how trade-offs can be cost-effective is not intuitive, so consider this example. Imagine that you have a choice between buying one of two cars. Both cars are identical in every way, except one includes a seat warmer option and costs more. Is the extra benefit worth the extra cost? Or, on the flipside of the same coin, is less benefit worth the lower cost? There is no correct answer. In trade-off scenarios, the decision maker’s willingness to pay determines which option is cost-effective. A “good value for the money” might depend on whether you live in a snowy climate or in the desert. Is the car with the seat warmer worth $5,000 more to you? What if it cost only $1,000 more? What if it cost only $5 more? Clearly, there is some point between $5 and $5,000 where most of you would pay more for more benefit, called the willingness-to-pay threshold.
This example illustrates two important concepts. First, when presented with the exact same cost-effectiveness data, different decision-makers (i.e., payers) can come to different conclusions because they have external considerations (e.g., budgets, population needs) that they bring to bear on the decision-making process. Second, cost-effectiveness analysis is a tool that packages and distills information from multiple sources to facilitate decision making – like using Consumer Reports data to buy a car – but does not, cannot, make the decision for you.
Now imagine that you are comparing two drugs: Drug A is the standard of care (comparator), while Drug B is new to the market and recently approved for prescribing. Let’s say the overall cost of Drug A is $14,000, which includes the cost of a course of the drug itself, the cost of treating potential side effects, and the cost of treatment failure. Let’s also say that the overall effectiveness of Drug A is .60, or a 60% chance of treatment success after accounting for the chance of side-effects and failure. Drug B has an incremental overall cost of $1,000 more (i.e., $15,000) and overall effect of .15 more (i.e., 0.75). In a real cost-effectiveness analysis, you would be sure to include all relevant comparators, perhaps also technologies C, D, E, and so on.
These cost-effectiveness results can be displayed on a graph called the cost-effectiveness plane. The vertical line represents overall costs, while the horizontal represents overall effectiveness. Drug A, the standard of care, is at the origin of the graph. Any health technologies plotted in the upper half of the graph are more costly than Drug A, and anything in the right half of the graph indicates more effectiveness than Drug A.
In this way, each quadrant represents one of four possible results. We can call these the northeast, southeast, southwest, and northwest quadrants. Drug B appears in the northeast quadrant because it costs more and has more effect than Drug A, representing a trade-off scenario. In which quadrant would a new technology appear if it cost more and was less effective than A? That’s right, the northwest quadrant, which is called dominated. In this case, we would favor the standard of care, Drug A, and automatically reject any new technology that falls into the northwest quadrant.
What does it mean if a new technology falls into the southeast quadrant? Correct again! The new technology costs less and is more effective than Drug A, called dominant, and would be automatically accepted. If Drug B ended up in either the northwest or southeast quadrants, we can stop right there, no further analysis is needed. The decision is clear. But this rarely happens.
Most of the time, technologies fall into the trade-off quadrants, requiring further consideration. And as you have now learned, Drug B may be considered cost-effective depending on whether it is considered “good value for the money”. To decide if it is a good value, it must be compared to the threshold, representing the maximum amount that the decision-maker is willing to pay.
Jason T. Hurwitz, MS, PhD
Jason T. Hurwitz is Assistant Director of the Center for Health Outcomes & Pharmacoeconomic Research (HOPE Center) at the University of Arizona. Dr. Hurwitz develops and teaches numerous HOPE Center training programs for healthcare industry professionals each year. He also conducts numerous industry, foundation, and federally funded studies in health economics and outcomes research (HEOR), most recently including a cost-effectiveness analysis of immunosuppressants for kidney transplant recipients and the development of a shared decision-making (SDM) tool to help patients and providers reduce the risk of drug-drug interactions. Follow Dr. Hurwitz on LinkedIn or visit https://www.pharmacy.arizona.edu/hope for upcoming training programs.