The apples and oranges of cost-effectiveness: A rejoinder

REAL-WORLD DATA TAKE YEARS

Finally, using the case of cyclooxygenase 2 inhibitors, the author raises the issue of sourcing data for cost-effectiveness studies.

There is some validity to this point regarding using only real-world experiential data versus data from randomized controlled clinical trials, as vastly different estimates of cost per unit of benefit can be found. However, strict adherence to this recommendation creates a dilemma: real-world data take years to accumulate after an intervention is approved for clinical use based on clinical trial data. But front-line clinicians and payers need to know whether the new intervention is worth adopting into daily clinical practice—particularly because new brand-name, patent-protected therapies generally cost much more early on than later, when patents expire and economies of scale induce drops in prices.

If high acquisition costs without supporting cost-effectiveness data preclude the adoption of the new therapy, then real-world experience cannot be accumulated. On the flip side, unfettered adoption would certainly consume significant resources that may turn out to have been wasted if, years later, real-world experience reveals that the effectiveness was significantly less than estimated by the clinical trial.

However, this is not a problem inherent in cost-effectiveness studies, but rather a result of the uncertainties and difficulties involved in translating findings from clinical trials to the real world, where patients are not as closely monitored to ensure proper compliance and to minimize side effects and uncontrolled interactions. Health economists are well aware of this problem of uncertainty and other limitations of randomized controlled trials.

These limitations have precipitated the development of decision analytic modeling for economic evaluation. This research method is now highly sophisticated and widely accepted as the gold standard. Decision analytic modeling allows data from a trial to be extrapolated beyond the trial period, intermediate clinical outcomes to be linked to final outcomes, clinical trial results to be generalized to other settings, head-to-head comparisons of interventions to be made where relevant clinical trial data do not exist, and economic evaluations to be performed for trials in which economic outcomes were not collected.³

Furthermore, decision analytic modeling in part exists to overcome the data issues raised by the commentary. By using probabilistic sensitivity analyses to account for uncertainties and assure robustness of the results, the reliability of the results is enhanced, regardless of the source of data. In fact, with today’s more powerful computers and software and the limited financial resources available for large randomized controlled clinical trials, the use of economic modeling continues to grow as an indispensable means of economic evaluation.

AN INDISPENSABLE TOOL

In conclusion, properly conducted cost-effectiveness studies are an increasingly important and indispensable tool as we strive to improve the efficiency and effectiveness of health care delivery, particularly in this time of health system changes, the aging of the population, and increasingly limited budgets. Economic modeling allows researchers to explore different scenarios, overcome many of the limitations of clinical trials, identify thresholds at which estimated cost-effectiveness ratios may change, and provide valuable information to health policy makers, providers, and patients to guide the efficient allocation and utilization of health care resources.