The Challenge of Endpoint Fit and Trial Emulation

Real-world evidence (RWE) underpins modern health economics and outcomes research (HEOR), especially when traditional clinical trials are infeasible in rare diseases. Small patient populations, varying symptoms, and uncertain etiology can limit trial insight and complicate endpoint selection. Observational RWE should be designed to emulate a target trial—pre-specifying eligibility, time zero, treatment strategies, follow-up, and outcomes—so that causal analyses can be judged by how closely they mimic a randomized trial (Hernán & Robins, 2016; Cashin et al., 2025). In practice, this also means choosing real‑world endpoints that reflect clinical goals and, where applicable, stakeholder priorities.

Using Primary Market Research to Identify Meaningful Outcomes

Primary market research (PMR) qualitative interviews, surveys, and cognitive testing—surfaces what actually matters to patients and clinicians before data extraction and analysis. FDA’s Patient‑Focused Drug Development (PFDD) guidance calls for qualitative methods to “obtain a deeper understanding of the patient experience,” and for translating those insights into measurable outcomes (U.S. Food & Drug Administration, 2022). In rare diseases, where symptom profiles and burden of illness are often multi‑faceted, this upstream work is critical. For example, Slade et al. (2018) emphasize involving patients throughout patient‑reported outcome measure (PROM) development to ensure content validity and interpretability. Cognitive interviewing then stress‑tests comprehension, recall windows, and response options, as reflected in validation work using Rasch modeling to revise instruments such as the SScQoL (Kocher et al., 2021; U.S. Food & Drug Administration, 2009).

What PMR Typically Uncovers (and Why It Matters)

  • Patient‑salient outcomes (function, fatigue, cognition, independence) that may be under‑captured by biomarker surrogates.
  • Decision points that define treatment initiation, rescue, or switching, and realistic follow-up windows.
  • Measurement details (recall periods, anchors, activities) required to operationalize endpoints in registries/EHRs and new PRO modules.
  • Preference and utility inputs via discrete‑choice experiments (DCE)/conjoint analysis for use in HE models (Bridges et al., 2011; Hauber et al., 2016).

Example: Where PMR Reorients Endpoints

While clinical programs often prioritize surrogate endpoints, PMR frequently reveals that patients value functional outcomes over purely laboratory measures. In phenylketonuria (PKU), beyond reductions in blood phenylalanine, patients often report priorities such as diet flexibility, cognitive clarity, and relief from daily burden. Qualitative work can map these priorities into a short, disease‑specific PRO module (with a 7–30‑day recall), followed by cognitive testing to refine wording and anchors. Similarly, qualitative studies in spinal muscular atrophy (SMA) document caregiver experiences and decision drivers around initiating disease‑modifying therapy, highlighting outcomes beyond survival that matter in daily life (Xiao et al., 2023).

From Insights to a Target‑Trial Specification

Use PMR outputs to fill the core fields of the emulated trial:

  • Eligibility: phenotype/genotype, age‑of‑onset bands, baseline function that match real clinic populations.
  • Strategies: the actual treatment pathways (initiation, titration, rescue) and comparators clinicians truly use.
  • Assignment/Time zero: the real‑world moment when decisions occur (first prescription, escalation, post‑exacerbation).
  • Outcomes: patient‑meaningful, clinically observable measures (validated PRO changes, motor milestones, rescue‑free days), with feasible ascertainment in the chosen data source. Transparent reporting improves reproducibility (Cashin et al., 2025).

Conclusion

For sponsors planning RWE submissions, registries, HEOR models, or label-expansion strategies, PMR ensures endpoints are both clinically sound and meaningful to regulators. Aligning rare‑disease RWE with outcomes that patients and clinicians value is indispensable. By embedding PMR at the start—interviews, surveys, cognitive testing—you specify fit‑for‑purpose endpoints and well‑posed causal questions before mining RWD. This upstream human input strengthens trial emulation, improves PROM validity, and ultimately produces real‑world evidence that better reflects clinical benefit in practice.

References (APA)

  • Bridges, J. F. P., Hauber, A. B., Marshall, D., Lloyd, A., Prosser, L. A., Regier, D. A., Johnson, F. R., & Mauskopf, J. (2011). Conjoint analysis applications in health—a checklist: A report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value in Health, 14(4), 403–413.
  • Cashin, A. G., Hróbjartsson, A., Hoffmann, T., Boutron, I., Ioannidis, J. P. A., & McCulloch, P. (2025). Transparent reporting of observational studies emulating a target trial (TARGET). JAMA.
  • Hauber, A. B., González, J. M., Groothuis-Oudshoorn, C. G. M., Prior, T., Marshall, D. A., Cunningham, C., Ijzerman, M. J., & Bridges, J. F. P. (2016). Statistical methods for the analysis of discrete choice experiments: A report of the ISPOR Conjoint Analysis Good Research Practices Task Force. Value in Health, 19(4), 300–315.
  • Hernán, M. A., & Robins, J. M. (2016). Using big data to emulate a target trial when a randomized trial is not available. American Journal of Epidemiology, 183(8), 758–764.
  • Kocher, A., Dzhambov, A., Boehncke, W. H., & Christiansen, H. (2021). Revision and validation of the German SScQoL using the Rasch model. Orphanet Journal of Rare Diseases, 16, 329.
  • U.S. Food & Drug Administration. (2009). Guidance for industry: Patient‑reported outcome measures: Use in medical product development to support labeling claims.
  • U.S. Food & Drug Administration. (2022). Patient‑Focused Drug Development: Methods to identify what is important to patients (Guidance 2).
  • Xiao, L., et al. (2023). Understanding caregiver experiences with disease‑modifying therapies for spinal muscular atrophy. Archives of Disease in Childhood, 108(11), 929–935.