THE NEED TO CONSIDER THE IMPACT OF DRIVERS OF RELATIVE TREATMENT EFFECTS BEYOND TREATMENT CHOICE IN COST-EFFECTIVENESS ANALYSES (CEA)

Author(s)

Warren Stevens, BA, MSc, PhD1, Lela Krackow, BA2, Cheryl Neslusan, PhD3.
1Principal, Medicus Economics, Milton, MA, USA, 2Analyst, Medicus Economics, Milton, MA, USA, 3Johnson and Johnson, Titusville, NJ, USA.

Presentation Documents

OBJECTIVES: Some HTA bodies rely heavily on estimates of cost-effectiveness that use methods focused on the question of what value is added by an innovation in isolation. Factors like where, how and when it will be used are rarely taken into account, beyond some consideration in broader deliberations by HTAs. In other industries, technologies are assessed ‘in place’ to ascertain their value. For example, when we picture the potential impact of introducing AI in healthcare, the where, how, and when this innovation will be used are key considerations. We explored the implications of ignoring such factors using data from studies that investigated treatment outcomes in patients with major depressive disorder (MDD).
METHODS: We leveraged empirical studies from the last decade to examine three hypothesized non-treatment drivers (time to diagnosis, time to treatment initiation, and type of provider) of the real-world effectiveness of treatments for MDD. The odds ratios for relative treatment effects, based on these factors, were compared with those from studies that estimated relative treatment effects considering treatment choice alone.
RESULTS: Studies investigating the impact of time to diagnosis, time to treatment initiation, and type of provider on relative treatment effects reported odds ratios ranging from 2.6-4.1, whereas a meta-analysis of twenty treatments reported a narrower range (1.4-1.9 compared to placebo). These data illustrate that non-treatment factors are key determinants of the variance in treatment effects. Therefore, if resource allocation decisions are informed by estimates that assume outcomes are not affected by the heterogeneity of patient experiences, or how and when treatments are delivered, outcomes will be suboptimal.
CONCLUSIONS: Contextual factors can be critical drivers of treatment effect variance. When assessing the value of a new technology, it is necessary to evaluate the extent to which factors beyond treatment choice matter to ensure that CEA estimates are informative for decision-makers.

Conference/Value in Health Info

2026-05, ISPOR 2026, Philadelphia, PA, USA

Value in Health, Volume 29, Issue S6

Code

EE206

Topic

Economic Evaluation

Topic Subcategory

Value of Information

Disease

No Additional Disease & Conditions/Specialized Treatment Areas

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