Proving LongTerm Value: Evidence Strategies Payers Trust 

Proving the long-term value of pharmaceutical assets remains one of the biggest challenges for manufacturers. In times of rising costs and economic uncertainties, European payers are more demanding, especially regarding the enduring benefits and sustained impact of new medicines.  

Payers and stakeholders view the long-term value of an asset as its ability to deliver sustained clinical benefits, economic efficiency, and positive health outcomes over time. For example: 

• In 2020, the German Federal Joint Committee (G-BA) commissioned the Institute for Quality and Efficiency in Health Care (IQWIQ) to develop a concept for routine practice data collection to further validate the outcomes of the added benefit assessment and inform further decisions regarding the gene therapy drug onasemnogene abeparvovec.¹ 

• In Sweden, for the 2019 assessment of lumakaftor + ivacaftor, the Dental and Pharmaceutical Benefits Agency (TLV) has been informed that in the event of a positive benefit decision, the National Treatment Council would require a follow-up via quality registers and also establish criteria for when treatment should be terminated.²  

Manufacturers are increasingly challenged to meet payer and stakeholder expectations and provide evidence that highlights the sustained clinical and economic benefits of their assets. In this article, we will discuss three complementary evidence strategies—health economic modelling, surrogate endpoints, and real-world evidence (RWE)—that can make payers comfortable investing in assets now that will show benefits in the longer time scales. 

Health-Economics Modelling– turning shortterm trials into lifetime value stories: 

Health economics applies economic principles and analytic methods to healthcare decisions, quantifying the costs and health gains of competing interventions, ultimately assessing how limited resources can be distributed to maximise population health.³ For each intervention, the lifetime costs and lifetime health gains (such as Quality Adjusted Life Years [QALYs]) are calculated. These calculations can extend beyond the trial cut-off, informing payers about the long-term use of an asset.³ This is especially crucial when payers have to decide whether to finance a high-cost, life-long therapy with only 12-24 months of follow-up data. Scenario testing (e.g., varying discount rates, alternative survival curves) shows decision makers that the manufacturer has tested every key assumption.  

When based on external real‑world data, such as, registries, natural‑history cohorts, or long‑term observational studies, these economic models not only satisfy technical guidelines from Health Technology Assessment (HTA) bodies such as the National Institute for Health and Care Excellence (NICE), TLV and the French National Authority for Health (HAS), but also provide quantitative estimates for outcome‑based payment deals, supporting negotiations of risk‑sharing mechanisms between parties.  

For example, for NICE HST11 (voretigene neparvovec for inherited retinal dystrophy), the manufacturer provided follow-up data of up to 4 years. However, the therapy was intended to be used for ~40 years and no long-term clinical data were provided during the submission.⁴ Instead, the potential benefit to the health system and the confirmation that voretigene neparvovec is an appropriate use of National Health Service (NHS) resources was demonstrated through the economic model. The committee accepted the model’s plausibility, which lead to a positive recommendation with a confidential commercial discount. However, not all payers consider health-economic modelling in their decision making, with, for example, the G-BA focusing on clinical data. 

Surrogate endpoints – accelerating access while data mature.  

Surrogate endpoints are intermediate biomarkers or clinical measures that can reliably predict hard outcomes such as overall survival or irreversible disability.⁵ When robustly validated, they allow manufacturers to generate earlier readouts, shorten trials and give payers an earlier signal of value. However, European HTA bodies will only accept them when supported by a clear biological rationale and quantitative validation showing a strong correlation between surrogate and final outcome—ideally via metaregression or patient-level concordance analyses.  

The joint NICE 2024 guidance stratifies surrogacy evidence into three tiers:⁵ 

• Level 1 (validated) – a treatment‑level relationship between changes in the surrogate and changes in the final outcome demonstrated across several randomised trials 

• Level 2 (consistent association) – patient‑ or cohort‑level correlation shown in observational or epidemiological data 

• Level 3 (biological plausibility) – mechanistic rationale without empirical confirmation 

France’s HAS Transparency Committee doctrine⁶ and the European network for Health Technology Assessment (EUnetHTA) JA2 surrogate‑endpoint guideline⁷ adopt comparable step‑wise criteria (biological plausibility → epidemiologic association → trial‑level validation), reinforcing a pan‑European consensus on how much weight each evidence tier should carry.  

To build trust, dossiers should include systematic validation analyses and sensitivity scenarios translating surrogate gains into long-term clinical benefit. Such processes can convert an accessible endpoint reading into an accelerator, as illustrated by HAS’s 2019 ASMR II (added medical benefit II) decision for emicizumab, which was based on annualised bleeding rate as a predictor of jointhealth preservation.⁸  

Real‑world evidence – validating effectiveness in routine practice 

RWE comprises observational data generated outside the strict confines of randomised trials (e.g., national disease registries, electronic health records, claims datasets and patient‑reported platforms) that show how a therapy performs across heterogeneous, comorbid populations and over much longer timespans than clinical and pivotal studies.⁹ European payers increasingly request RWE obligations in managed‑entry agreements because real‑life utilisation, adherence and outcomes can diverge sharply from trial projections.  

Flagship infrastructure initiatives such as the European Data Analysis and Real-World Interrogation Network (DARWIN EU)¹⁰ and the European Health Data & Evidence Network (EHDEN)¹¹ are mapping millions of electronic health‑records and claims entries into the Observational Medical Outcomes Partnership (OMOP)¹² common data model and deploying a federated analytics platform. This “network‑of‑networks” approach means regulators or HTA bodies can release a pre‑agreed study protocol and retrieve aggregated results from more than 100 hospitals, primary‑care and biobank datasets across Europe within weeks rather than years. Alongside, individual HTA agencies are publishing methodological blueprints:  

• NICE’s 2022 RWE Framework⁹ sets out mandatory quality‑assessment checklists and study‑registration rules 

• Germany’s G‑BA/PEI ABDE guidance (2023)¹³ details statistical and governance standards for registry‑triggered benefit dossiers  

• France’s HAS Post‑Registration Study doctrine (2021)¹⁴ defines minimum dataset, follow‑up and analytic‑plan expectations 

Together, these projects and templates give manufacturers a clear, pan‑European playbook for generating decision‑grade RWE. High‑quality RWE strengthens dossier assumptions, tests surrogate‑endpoint predictions and activates outcome‑based rebates. For example, the Italian Medicines Agencies (AIFA) hepatitis‑C registry verified real‑world sustained virological response with direct‑acting antivirals and automatically recalculated price according to performance.¹⁵ 

How to build a value dossier that withstands scrutiny? 

Health-economic modelling, use of surrogate endpoints and RWE in combination can be a powerful tool to leverage negotiations with payers. However, individually, they might not be as convincing. All three methods require planning and validation to fully translate the long-term value of the asset. To fully utilise them when building a value dossier, the following strategies should be taken: 

Health economics modelling: 

1. Start evidence planning early – take into account the health economic model structure when designing phase II studies. 

2. Select transparent extrapolation techniques for long-term outcomes – justify parametric choices & external data. 

3. Scenariotest alternative discount rates & time horizons per European payer guidelines. 

4. Embed uncertainty analysis – Probabilistic sensitivity analysis (PSA) or Expected Value of Perfect Information (EVPI). 

5. If submitting a budget impact model (BIM), ensure that it is transparent, based on credible assumptions and uses accepted methodologies. 

Use of surrogate endpoints: 

1. Prealign with surrogate validation standards (NICE/European Medicines Agency [EMA]) for each endpoint. 

Real-world evidence generation: 

1. Leverage European & national registries – map data availability and the General Data Protection Regulation (GDPR) hurdles. 

2. Define a robust RWE study protocol – target cohorts, comparators, statistical plan. 

3. Plan evidence updates postlaunch – commit to timelines & decision checkpoints. 

General strategies for building a value dossier: 

1. Craft payer value narrative – concise visuals, clear assumptions, risksharing agreements (to reduce uncertainties by sharing financial risks). 

2. Prepare parallel consultation with HTA bodies under the EU HTA Regulation. 

3. Ensure the value dossier content aligns with key HTA templates (e.g. the Joint Clinical Assessment [JCA] template).  

4. Include an objection handler.  

5. Include clinical and economic systematic literature reviews (SLRs) – could come under one with evidence base (e.g., trials, RWE, SLR).  

European payers are increasingly more willing to offer premium prices, especially when risks and evidence uncertainties are transparently managed. By combining healtheconomic extrapolations, validated surrogate endpoints and real-world evidence, manufacturers can craft a cohesive value narrative that withstands even the toughest HTA negotiations. The 2025 EU HTA Regulation and the introduction of the JCA will amplify the need for early, cross-member-state alignment and diligent evidence updates.  

If you are preparing a value dossier or negotiating a managedentry agreement, Remap’s Market Access team can stresstest your assumptions, model alternative scenarios, and design rapidcycle RWE studies, turning payer uncertainty into sustainable patient access and revenue. Contact us HERE to discuss.

References

1. IQWiQ, 2020, Concept for a routine practice data collection according to the Law for More Safety in the Supply of Medicines (GSAV) – onasemnogene abeparvovec – https://www.iqwig.de/download/a20-61_anwendungsbegleitende-datenerhebung-onasemnogen-abeparvovec_rapid-report_v1-0.pdf 
2. TLV, 2019, Basis for decision on subsidy – New application Committee for Pharmaceutical Benefits Orkambi (lumacaftor + ivacaftor) – https://www.tlv.se/download/18.500ea4181641067957a31c3f/1529587605088/bes180614_orkambi_underlag.pdf 
3. NICE, 2025, NICE health technology evaluations: the manual – https://www.nice.org.uk/process/pmg36 
4. NICE, 2019, Voretigene neparvovec for treating inherited retinal dystrophies caused by RPE65 gene mutations – highly specialised technologies guidance – https://www.nice.org.uk/guidance/hst11 
5. NICE, et. al., 2024, Surrogate endpoints in cost-effectiveness analysis for use in health technology assessment white paper – https://www.cda-amc.ca/sites/default/files/MG%20Methods/surrogate-endpoints-report.pdf 
6. HAS, 2020, Transparency Committee doctrine Principles of medicinal product assessments and appraisal for reimbursement purpose – https://www.has-sante.fr/upload/docs/application/pdf/2019-07/doctrine_de_la_commission_de_la_transparence_-_version_anglaise.pdf 
7. EUnetHTA, 2015, Guideline – Endpoints used in Relative Effectiveness Assessment: Surrogate Endpoints – https://tools.eunethta.be/files/Endpoints%20in%20REA%20-%20Surrogate.pdf 
8. HAS, 2018, Transparency Committee opinion of Hemlibra for prophylaxis to prevent bleeding episodes in patients with hemophilia A who have developed an anti-factor VIII inhibitor – https://www.has-sante.fr/jcms/c_2868847/en/hemlibra-emicizumab-haemostatic 
9. NICE, 2022, NICE real-world evidence framework – https://www.nice.org.uk/corporate/ecd9/chapter/overview 
10. https://www.ema.europa.eu/en/about-us/how-we-work/data-regulation-big-data-other-sources/real-world-evidence/data-analysis-real-world-interrogation-network-darwin-eu 
11. https://www.ehden.eu/ 
12. https://www.ohdsi.org/data-standardization/ 
13. G-BA, 2023, Data collection for new medicinal products during use – https://www.g-ba.de/themen/arzneimittel/arzneimittel-richtlinie-anlagen/anwendungsbegleitende-datenerhebung/ 
14. HAS, 2021, Real-world studies for the assessment of medicinal products and medical devices – https://www.has-sante.fr/upload/docs/application/pdf/2021-06/real-world_studies_for_the_assessment_of_medicinal_products_and_medical_devices.pdf 
15. AIFA, 2015, Nota AIFA relativa ai Registri per il monitoraggio dell’epatite C – importanza della compilazione delle schede SVR. – https://www.aifa.gov.it/sites/default/files/Pillola_n_910_21_12_2015_0.pdf