The apples and oranges of cost-effectiveness
Measures of cost-effectiveness are used to compare the merits of diverse medical interventions. A novel drug for metastatic melanoma, for instance, can be compared with statin therapy for primary prevention of cardiovascular events, which in turn can be compared against a surgical procedure for pain, as all are described by a single number: dollars per life-year (or quality-adjusted life-year) gained. Presumably, this number tells practitioners and payers which interventions provide the most benefit for every dollar spent.
However, too often, studies of cost-effectiveness differ from one another. They can be based on data from different types of studies, such as randomized controlled trials, surveys of large payer databases, or single-center chart reviews. The comparison treatments may differ. And the treatments may be of unproven efficacy. In these cases, although the results are all expressed in dollars per life-year, we are comparing apples and oranges.
In the following discussion, I use three key contemporary examples to demonstrate problems central to cost-effectiveness analysis. Together, these examples show that cost-effectiveness, arguably our best tool for comparing apples and oranges, is a lot like apples and oranges itself. I conclude by proposing some solutions.
PROBLEMS WITH COST-EFFECTIVENESS: THREE EXAMPLES
Studies of three therapies highlight the dilemma of cost-effectiveness.
Example 1: Vertebroplasty
Studies of vertebroplasty, a treatment for osteoporotic vertebral fractures that involves injecting polymethylmethacrylate cement into the fractured bone, show the perils of calculating the cost-effectiveness of unproven therapies.
Vertebroplasty gained prominence during the first decade of the 2000s, but in 2009 it was found to be no better than a sham procedure.1,2
In 2008, one study reported that vertebroplasty was cheaper than medical management at 12 months and, thus, cost-effective.3 While this finding was certainly true for the regimen of medical management the authors examined, and while it may very well be true for other protocols for medical management, the finding obscures the fact that a sham procedure would be more cost-effective than either vertebroplasty or medical therapy—an unsettling conclusion.
Example 2: Exemestane
Another dilemma occurs when we can calculate cost-effectiveness for a particular outcome only.
Studies of exemestane (Aromasin), an aromatase inhibitor given to prevent breast cancer, show the difficulty. Recently, exemestane was shown to decrease the rate of breast cancer when used as primary prevention in postmenopausal women.4 What is the costeffectiveness of this therapy?
While we can calculate the dollars per invasive breast cancer averted, we cannot accurately calculate the dollars per life-year gained, as the trial’s end point was not the mortality rate. We can assume that the breast cancer deaths avoided are not negated by deaths incurred through other causes, but this may or may not prove true. Fibrates, for instance, may reduce the rate of cardiovascular death but increase deaths from noncardiac causes, providing no net benefit.5 Such long-term effects remain unknown in the breast cancer study.
Example 3: COX-2 inhibitors
Estimates of cost-effectiveness derived from randomized trials can differ from those derived from real-world studies. Studies of cyclooxygenase 2 (COX-2) inhibitors, which were touted as causing less gastrointestinal bleeding than other nonsteroidal anti-inflammatory drugs, show that cost-effectiveness analyses performed from randomized trials may not mirror dollars spent in real-world practice.
Estimates from randomized controlled trials indicate that a COX-2 inhibitor such as celecoxib (Celebrex) costs $20,000 to prevent one gastrointestinal hemorrhage. However, when calculated using real-world data, that number rises to over $100,000.6
TWO PROPOSED RULES FOR COST-EFFECTIVENESS ANALYSES
How do we reconcile these and related puzzles of cost-effectiveness? First, we should agree on what type of “cost-effectiveness” we are interested in. Most often, we want to know whether the real-world use of a therapy is financially rational. Thus, we are concerned with the effectiveness of therapies and not merely their efficacy in idealized clinical trials.
Furthermore, while real-world cost-effectiveness may change over time, particularly as pricing and delivery vary, we want some assurance that the therapy is truly better than placebo. Therefore, we should only calculate the cost-effectiveness of therapies that have previously demonstrated efficacy in properly controlled, randomized studies.7
To correct the deficiencies noted here, I propose two rules:
- Cost-effectiveness should be calculated only for therapies that have been proven to work, and
- These calculations should be done from the best available real-world data.
When both these conditions are met—ie, a therapy has proven efficacy, and we have data from its real-world use—cost-effectiveness analysis provides useful information for payers and practitioners. Then, indeed, a novel anticancer agent costing $30,000 per life-year gained can be compared against primary prevention with statin therapy in patients at elevated cardiovascular risk costing $20,000 per life-year gained.
