February 15, 2019 at 5:00 am ET
For the past two decades, a plethora of organizations identified by various innocuous acronyms (e.g., NICE — National Institute for Health and Care Excellence) has tried to convince policymakers, providers and patients among others that economic analysis or modeling ought to be used to determine drug coverage decisions.
They have tried to define value and insert value as a component into models so that they may analyze the appropriateness of payment for a pharmaceutical. They have instituted arbitrary parameters regarding the inclusion of data in their economic models. They have even tried to sell analytic findings to audiences with promises of substantial savings.
So are these groups wrong in pressing for the use of their economic modeling and analysis in determinations about whether payers ought to cover life-saving medicines?
The answer is yes and no. These organizations are utilizing legitimate analytical tools developed by economists. Economic analysis such as Cost per Quality of Life Years Saved, Cost Effectiveness or Budget Impact Analysis can be used to analyze information for decision-makers in various components of the economy, such as wearing helmets while operating a motorcycle. However, these analytical techniques were never meant to be applied as the ultimate arbiter of whether a medicine was worth paying for.
For starters, if QALY, often favored by NICE and the Institute for Clinical and Economic Research is the intended analytical technique, then one needs to define “value” for each therapy. However, the complexity of defining value is an immense undertaking, especially in an era of personalized medicine.
For example, imagine defining a set of values for patients suffering from one type of disease like diabetes. Now imagine defining a set of values for the same patients when you consider their co-morbidities (e.g., a patient suffering from diabetes and cardiovascular disease or cancer) and then add to the complexity by identifying individual values of each patient based on their socioeconomic background (e.g., race and ethnicity).
Also, patients often have different priorities regarding health outcomes they prefer. They are more likely to follow the treatment prescribed if it is consistent with what they see as a good outcome.
Thus, the inherent problem of organizations instituting such “value” models is that patients who don’t fit a particular profile are denied therapies that can help manage or even cure their disease and, in some cases, save their lives. Just as no individual’s health is “average,” no individual’s life choices and values are average.
Next, the intent of such economic modeling also has to be taken into consideration. Economists wanted the analysis to be a guide rather than a final answer or a mandate.
They intended for decisions to be made based on economic modeling that was used in combination with other data points, such as expert opinions and outcomes data based on past patients. Subsequently, the enforcement of medical decisions based solely on one set of analytics restricts access for patients in need of essential therapies that require a different set of data points.
Finally, the analysis was never meant to be static or developed based on static information. The world of data, and specifically health care data, evolves rapidly. The intent was that the analytic model would be developed, nurtured and refined over time with input from a multitude of sources, not just a single researcher.
For instance, every time a physician diagnoses and provides treatment to a patient and inputs that information, the model becomes even more robust, providing users with greater statistical confidence. More importantly, such models do not consider the potential impact of new medicines in real time and are based on the treatment effect of an average patient, which has a retrospective view of medical evidence.
In the end, there are significant methodological concerns about the development of analytic models using legitimate economic techniques. However, much more scrutiny is needed concerning how such models are applied when making coverage decisions for life-saving biopharmaceuticals.
Robert Popovian is the vice president of Pfizer U.S. Government Relations.
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