Research into major depressive disorder (MDD) highlights how a condition can manifest differently across individuals, varying in presentation, underlying causes and treatment response. This makes personalized, patient-centered care essential. Yet despite its prevalence, depression is typically treated with a “one-size-fits-all” approach that fails to reflect the reality that no two cases are the same.
In the U.S. alone, more than 47.8 million Americans live with depression. While medications work for some, a study of the National Health and Nutrition Examination Survey (NHANES) data found that 56.5% of patients on oral antidepressant treatment for at least 3 months reported experiencing residual or ongoing depressive symptoms while on treatment.
Two recent studies supported by Johnson & Johnson explored the factors that shape how well MDD treatments work in the real world. One examines the idea that real-world outcomes are shaped not just by medicine, but by the full context in which care is delivered. The other examines heterogeneity or differences in how patients respond to treatment and what they value in their care. Together, they suggest that models of MDD treatment value and resource allocation should account for the individual patients behind the data, rather than relying only on average effects observed under conditions that are not sufficiently reflective of the real world.
How do we value medicines when treatment choice is only part of the story?
When stakeholders assess the value of a new technology, they typically consider cost effectiveness estimates that focus on the value added by an innovation in isolation. Factors such as where, how and when the technology will be used are rarely fully incorporated into these calculations.
The first study examined this by reviewing real-world MDD studies from 2005 to 2025 to identify factors driving variation in treatment outcomes beyond the treatment itself. Three non-treatment factors emerged as significant drivers: duration of untreated illness, prior treatment experience, and type of prescribing provider. When resource allocation decisions are based on models that assume contextual factors don’t matter, the result can be misallocation of resources and patients who fall short of their potential.
The second study examined heterogeneity not only in how patients respond to treatment but also in what they want from it. Payers often design coverage and utilization management around population-average outcomes; this approach breaks down when treatment effects and patient preferences vary.
The review found substantial evidence of treatment response heterogeneity in MDD, varying across inflammatory biomarkers (measurable substances in the body), metabolic characteristics, neurophysiology, clinical features, and demographics. Many key modifiers of treatment response are visible to providers and patients but not captured in payer claims data, creating hidden health and utilization costs when formularies restrict access.
Evidence of marked heterogeneity in patients’ preferences for different treatment attributes was also found in the literature. For example, younger patients expressed a stronger preference for faster symptom relief compared to older patients. Additionally, those with more severe depression expressed greater willingness to accept a higher risk of adverse events for greater efficacy compared to those with less severe depression. In terms of treatment delivery, older patients favored structured, high-touch formats, while younger patients preferred individual or digital interventions.
How should resources be allocated?
Value assessment frameworks, coverage policies, and utilization management approaches that rely primarily on average effects may miss important differences in who benefits, under what circumstances, and at what cost. Population-level resource allocation decisions that do not account for this variation may lead to inefficient use of resources and poorer health outcomes. Accommodating this heterogeneity through open formularies and shared decision-making can improve treatment adherence, leading to better patient outcomes and reduce wasteful healthcare spending.
The research was funded by Johnson & Johnson.
For full details on the study designs, methods and limitations see: Stevens W, Krackow L, Neslusan C. The Need to Consider the Impact of Drivers of Relative Treatment Effects Beyond Treatment Choice in Cost-Effectiveness Analyses. Johnson & Johnson Innovative Medicine. Poster presented at: The International Society for Health Economics and Outcomes Research (ISPOR); May 17-20, 2026; Philadelphia, PA.
Shafrin J, Zawadzki N, Neslusan C. The Extent of Treatment Response and Preference Heterogeneity in Major Depressive Disorder: Implications for Population-level Resource Allocation. Poster presented at: The International Society for Health Economics and Outcomes Research (ISPOR); May 17-20, 2026; Philadelphia, PA.
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