Oncology market access strategy depends on more than clinical evidence alone. This analysis examines how value differentiation signals (beyond clinical outcomes) vary across Germany and the United States for breast cancer therapies. Using GPI’s Horizon framework, we break down how product characteristics, trial design, and disease burden each contribute to how therapies are positioned, and what that means for pharmaceutical teams shaping evidence generation plans.
Breast cancer remains one of the most active areas of oncology innovation, with multiple targeted therapies entering the treatment landscape over the past decade. Clinical outcomes such as improvements in survival, disease progression, and safety profiles remain fundamental to how new therapies are evaluated by regulators and health technology assessment (HTA) bodies.
However, once therapies have demonstrated meaningful clinical benefit and enter the treatment landscape, the factors that shape how they are differentiated may extend beyond clinical evidence alone. In practice, healthcare decision-makers often consider a broader set of attributes when evaluating treatments within a therapeutic class. These can include the burden of disease addressed by the therapy, product characteristics, and the robustness of clinical trial design.
As treatment landscapes become more crowded, understanding how these additional signals are interpreted across healthcare systems becomes increasingly important for pharmaceutical teams shaping development strategies and evidence generation plans.
To explore these dynamics, we analysed a selection of established breast cancer therapies across two major healthcare systems: Germany and the United States. The analysis was conducted using Global Pricing Innovations’ Horizon™ framework, which evaluates therapies across multiple value domains using a structured multi-criteria approach.
Therapies were assessed using publicly available evidence sources, including G-BA assessment documentation and FDA regulatory materials, combined with market-specific payer weightings within the Horizon framework. For the purposes of this analysis, we focused specifically on value signals beyond clinical outcomes, including burden of disease, product characteristics, and trial design, alongside other contextual considerations.
What drives oncology market access differentiation beyond clinical outcomes?
How differentiation signals vary across markets
The analysis suggests that while similar value signals are considered across healthcare systems, their relative contribution to how therapies are differentiated can vary across markets.
Figure 1. Relative contribution of differentiation signals beyond clinical evidence (Germany vs United States)
Across the therapies analysed, companies mainly try to differentiate through product-related features in Germany, which make up nearly half of all differentiation signals. These include aspects like innovation, formulation, and expected treatment impact, influencing how therapies are positioned in the treatment landscape. This also reflects the German setting, where stricter evaluation criteria mean companies have fewer opportunities to differentiate through trial design. For example, there is less flexibility around comparator choice, endpoints, and overall study design, limiting how much differentiation can be achieved through evidence generation alone.
Disease burden and trial design contribute similar shares of differentiation signals in Germany, each representing roughly a quarter of the overall composition.
In the United States, trial design plays a larger role, contributing approximately 34% of differentiation signals compared with around 27% in Germany. This suggests that aspects such as study design, comparator selection, and the robustness of evidence generation may play a more prominent role in shaping how therapies are evaluated in the US healthcare context.
Taken together, these findings suggest that while product-related attributes remain a key differentiator across markets, the relative importance of other signals such as disease burden and trial design, varies across healthcare systems.
How does Germany’s approach to therapy differentiation differ from the United States?
Differentiation profiles across breast cancer therapies
Examining market-level trends provides useful system-level insight, but it is equally important to understand how individual therapies perform across value domains within a treatment landscape.
Using the Horizon™ framework, the selected breast cancer therapies were evaluated across the value domains described above using publicly available evidence sources and market-specific payer weightings. This structured approach enables therapies to be examined consistently across a range of non-clinical differentiation signals.
Legend:
🟢 Strong differentiation signal | 🟡 Moderate differentiation signal | 🔴 Limited or no differentiation signal
Figure 2A. Differentiation signals across selected breast cancer therapies – Germany
Figure 2B. Differentiation signals across selected breast cancer therapies – United States
The resulting analysis highlights that therapies demonstrate distinct differentiation profiles across domains. Some treatments appear to perform particularly strongly in areas such as trial design robustness, while others differentiate through product characteristics or innovation-related attributes.
Viewing therapies through this lens illustrates how differentiation within a therapy landscape can be shaped by multiple signals beyond clinical outcomes alone. By decomposing these signals into structured domains, the Horizon framework enables a more systematic view of how therapies may be positioned relative to one another.
See how GPI Horizon evaluates your asset across value domains.
Horizon applies the same structured MCDA approach used in this analysis to your specific therapy, comparator set, and target markets, generating a differentiation profile in hours rather than weeks.
What is multi-criteria decision analysis (MCDA) and how is it applied in oncology?
Understanding differentiation signals through structured frameworks
The analysis presented here draws on Global Pricing Innovations’ Horizon™ framework, a structured methodology designed to evaluate the value attributes of healthcare interventions.
GPI Horizon™ applies a multi-criteria decision analysis (MCDA) approach, capturing the multiple dimensions of value commonly considered in payer decision-making. These include:
- Burden of disease – patient population size, disease severity, and unmet need
- Product characteristics – innovation, formulation, and treatment impact
- Trial design – study phase, comparator selection, and evidence robustness
- Clinical benefit – efficacy, safety, and quality-of-life outcomes
- Other contextual considerations – including economic uncertainty, HTA perspectives, and public health impact
While clinical evidence remains the foundation of value assessment, structured frameworks such as Horizon allow analysts to examine how additional signals contribute to differentiation once therapies have demonstrated clinical benefit and entered the treatment landscape.
Why do market-specific value signals matter for evidence generation planning?
Implications for pharmaceutical teams
As oncology pipelines continue to expand, understanding how therapies are differentiated across markets becomes increasingly important. In therapy areas such as breast cancer where multiple treatments may demonstrate meaningful clinical benefit, value differentiation may depend on a broader set of attributes beyond clinical endpoints alone.
Our analysis suggests that the relative importance of these attributes may vary between markets. Product-related features appear to contribute more strongly to value differentiation in Germany, while the robustness of clinical trial design contributes more prominently to value perception in the United States.
For pharmaceutical teams, these findings highlight the importance of considering market-specific value signals when shaping development strategies, evidence generation plans, and market access approaches.
Looking ahead
Healthcare systems are increasingly evaluating therapies across multiple dimensions of value. Structured analytical frameworks such as GPI Horizon™ provide a systematic way to interpret these signals, enabling analysts to move beyond simple comparisons and instead explore how value is constructed across therapy landscapes.
In rapidly evolving fields such as breast cancer, understanding how value signals differ across healthcare systems can provide valuable insights for navigating increasingly complex evidence and reimbursement environments.
FAQs
A: Beyond clinical evidence, breast cancer therapies are differentiated through three main signals: product characteristics (covering innovation, formulation, and expected treatment impact), disease burden (including patient population size, severity, and unmet need), and trial design (study phase, comparator selection, and evidence robustness). The relative weight of each signal varies by healthcare system.
A: In Germany, product-related features account for nearly half of non-clinical differentiation signals, reflecting the constrained flexibility around evidence generation under the G-BA framework. In the United States, trial design contributes a larger share (approximately 34% compared with around 27% in Germany), suggesting that study robustness and comparator selection carry more weight with US payers.
A: GPI Horizon is a multi-criteria decision analysis (MCDA) framework designed to evaluate the value attributes of pharmaceutical products across multiple domains: burden of disease, product characteristics, trial design, clinical benefit, and other contextual considerations. It enables pharmaceutical teams to assess how therapies compare within a treatment landscape using a structured, systematic approach.
A: Oncology market access planning requires understanding which value signals carry weight in each target market allows pharmaceutical teams to align evidence generation plans earlier in development. Where product-related features drive differentiation (as in Germany), teams can focus on innovation and formulation positioning. Where trial design dominates (as in the United States), investment in comparator selection and study robustness is likely to yield the greatest return at the reimbursement stage.
Want to map value differentiation signals for your oncology asset?
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