How primary research fills evidence gaps and strengthens U.S. payer-ready
economic and real-world evidence.
HEOR models are only as credible as the inputs behind them. In rare diseases, those inputs are
frequently incomplete: natural history is poorly characterized, endpoints are inconsistent, and small,
fragmented patient populations amplify uncertainty in both clinical and economic parameters (Grand et
al., 2024; Pearson et al., 2018). When published literature, registries, and administrative datasets cannot
provide decision-grade evidence, primary market research (PMR) becomes a practical way to generate
the missing inputs needed for robust economic evaluations and real-world evidence (RWE) programs
(Dang, 2023).
Improving U.S. payer relevance and strengthening RWE
U.S.-focused PMR (including interviews with American clinicians and payers) helps align models with
U.S. sites of care, practice patterns, and cost components relevant to Medicare and commercial plans.
This improves dossier credibility because it supports the type of structured clinical and economic
evidence exchange described in the AMCP Format for Formulary Submissions (AMCP, 2024). PMR can
also strengthen RWE strategy by informing observational endpoints, baseline severity characterization,
and assumptions that are otherwise unobservable in routine datasets; modern RWE guidance recognizes
patient-generated data as one of several real-world data sources (Dang, 2023).
Why rare-disease evidence gaps matter
Rare-disease reimbursement and coverage decisions often hinge on uncertainty. Progression rates, real-
world treatment pathways, health-related quality of life (HRQoL), and true cost burden are frequently
under-documented, leading to challenges to sensitivity analyses. While reduced or generic information
can help with creating the model, it creates a potential mode of failure, by designing models that are
technically correct but operationally unconvincing -This is because the underlying assumptions cannot
be defended with robustness in a payer discussion (Grand et al., 2024; Pearson et al., 2018).
Where PMR strengthens HEOR input
PMR using patient and caregiver surveys, clinician interviews, – can convert high impact assumptions
into model-ready parameters. The goal is to close the gaps that most materially drive model outputs and
stakeholder confidence. In fact, clinicians’ interviews clarify real world monitoring cadence, switching
triggers, and identifying real world clinical pathways that help HEOR models reflect real world clinical
practice. Surveys can also help quantify healthcare resource utilization that may be incomplete in claims
alone. Patient and Caregiver research also captures indirect costs, lost work time, caregiving intensity,
and out of pocket costs that can materially shift cost-of-illness and budget impact assumptions.
PROs, utilities, and patient preferences (with the right technical guardrails)
Rare-disease HEOR often lacks real world utility values and patient-centered outcomes as evidence. PMR
can measure HRQoL in the relevant severity mix and quantify caregiver spillover—critical in pediatric
and high-burden conditions (Grand et al., 2024). PMR can also elicit preferences using discrete choice
experiments (DCEs), producing preference weights that strengthen scenario testing and value
communication. Importantly, to use DCE-based values on the 0–1 QALY scale, latent preference weights
must be anchored to the full health (1) to dead (0) scale using accepted anchoring approaches (Wang et
al., 2023; Ramos-Goñi et al., 2013).
Practical next step
Run a parameter audit on your rare-disease model. Identify the three inputs that most influence
results—often pathways, utilities, and levers of switching and discontinuation, then let MedPanel
prioritize PMR to replace the weakest assumptions with RWE. The outcome is not just a cleaner model;
it is a stronger, more defensible value story.
To get started, browse our recruitment pages to discover tailored solutions for reaching the right patient populations.
References
- Academy of Managed Care Pharmacy (AMCP). (2024). AMCP Format for Formulary Submissions,
Version 5.0. Journal of Managed Care & Specialty Pharmacy, 30(4-b Suppl).
https://www.amcp.org/sites/default/files/2024-04/AMCP-Format-5.0-JMCP-web_0.pdf - Dang, A. (2023). Real-World Evidence: A Primer. Pharmaceutical Medicine, 37(1), 25-36.
https://doi.org/10.1007/s40290-022-00456-6 - Grand, T. S., Ren, S., Hall, J., Oudin Åström, D., Regnier, S., & Thokala, P. (2024). Issues, Challenges
and Opportunities for Economic Evaluations of Orphan Drugs in Rare Diseases: An Umbrella Review.
Pharmacoeconomics, 42(6), 619-631. https://doi.org/10.1007/s40273-024-01370-2 - Pearson, I., Rothwell, B., Olaye, A., & Knight, C. (2018). Economic Modeling Considerations for Rare
Diseases. Value in Health, 21(5), 515-524. https://doi.org/10.1016/j.jval.2018.02.008 - Ramos-Goñi, J. M., Rivero-Arias, O., Errea, M., Stolk, E. A., Herdman, M., & Cabasés, J. M. (2013).
Dealing with the health state 'dead' when using discrete choice experiments to obtain values for EQ-
5D-5L health states. The European Journal of Health Economics, 14(6), 853-863.
https://doi.org/10.1007/s10198-013-0505-1 - Wang, H., Luo, J., Jiang, L., Regier, D., & Brazier, J. (2023). Discrete Choice Experiments in Health
State Valuation: A Systematic Review of Progress and New Trends. Applied Health Economics and
Health Policy, 21(3), 405-418. https://doi.org/10.1007/s40258-023-00794-9
