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Value Assessment and Heterogeneity: Another Side to the Story
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Value Assessment and Heterogeneity: Another Side to the Story

Steven D. Pearson, MD, MSc
The founder and president of the Institute for Clinical and Economic Review responds to the commentary on heterogeneity in value assessment.
Am J Manag Care. 2019;25(11):542-543
To assert that value assessment is at fault for ignoring heterogeneity in relative effectiveness, or for minimizing the importance of subgroups (the relationship between the heterogeneity and subgroups being critical but often obscured), is a bit like finding a man building a house out of the wood he can find or borrow from neighbors and criticizing him for not using bricks that no one will sell him. He has a need for shelter; he does the best he can with the resources he can get; and he would love to have bricks, but powers beyond his control make that impossible.

Let’s start with the goal of value assessment. What are we trying to build? Is the aim to provide a tool to help inform the clinical care of individual patients? Value assessment can indeed be oriented to serve the interests of enhanced shared decision making for individual patients.1 Evidence-based tools can help frame the many different elements of clinical decisions that are important to patients and provide summaries of evidence from population averages or, ideally, from results for patients with similar clinical characteristics. What most distinguishes this form of patient-targeted value assessment is that it keeps all the various elements of risks, benefits, and other elements of value disaggregated so patients can place their own unique “weights” on them and add them up or otherwise consider them in some quasi or formally quantitative process.

This is value assessment in service of what I would call individual heterogeneity—the variation among individual patients that a skilled clinician can illuminate and apply to tailor the care for a patient in their best interest. Individual patients will have unique clinical, emotional, social, and other characteristics that providers should always consider to help select the “best” drug or other treatment option, a critical goal of good medical care.

But there is a second kind of heterogeneity that can be called population heterogeneity, and this is the home territory of the value assessment performed by health technology assessment (HTA) agencies and research groups around the world. The goal of HTA here is not to inform individual clinical decisions but to inform the decisions that are taken at the population level: coverage and pricing. The heterogeneity that matters most in these decisions reflects variation in outcomes at a higher level than the individual and has two forms: one that is knowable in advance of treatment and one that is unknowable to the patient and clinician because its causes are unknown to all. In the latter case, evidence may show that patient outcomes appear something like a bell curve, with some patients receiving “average” benefits and harms, whereas others experience better or worse outcomes. The key feature of unknowable population heterogeneity is that there are no signposts, biomarkers, or key clinical indicators that can helpfully predict whether a specific patient will have average outcomes. Although it is still helpful to understand the range of outcomes for different patients, unknowable population heterogeneity leaves patients, clinicians, and policy makers largely reliant on population averages.

However, sometimes evidence can provide a guide to help identify when patients can be expected to experience relatively better or worse outcomes. And here lies the connection to subgroup analysis. Formal subgroup analysis is the most powerful way for HTA to identify how the risks and benefits of treatment may vary systematically within a larger population.2 I would argue that it is misleading to claim that HTA has been slow or recalcitrant in recognizing, seeking, and applying subgroup information to create precise value assessments at the population level. Seeking subgroups for which a drug might be most effective and cost-effective is a vigorous part of HTA. As one example, at the National Institute for Health and Care Excellence in the United Kingdom, this effort leads the agency to designate positive funding decisions for subgroups for many drugs that would otherwise fail a general test of cost-effectiveness across the entire labeled population.3


 
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