Real-World Evidence Helps Accelerate Formulary Decisions for Biologics

Randomized trials remain the gold standard for establishing efficacy, but their strict populations often leave payers with knowledge gaps about how biologics perform in routine care. Real-world evidence (RWE) derived from sources like electronic health records (EHRs), insurance claims, and patient registries is now bridging those gaps and speeding up coverage decisions for biologics.

Real-world evidence bridges gaps in randomized trials | Image credit: metamorworks | stock.adobe.com

A recent survey of US payers found that 85% use RWE to guide formulary placement in oncology, primarily to compare treatment effectiveness when head-to-head trials are lacking and to validate guideline recommendations. The most frequently used RWE sources were administrative claims data (79% of payers), clinical/EHR data (69%), prospective observational cohorts (60%), and disease registries (37%).¹ Reflecting a broader trend, regulators and payers have embraced RWE to enable earlier access to innovative therapies despite initial uncertainties. For example, some health technology assessment agencies now grant conditional formulary approval for high-cost biologics while additional real-world outcomes data are gathered to confirm value.²

Advances in data analytics and study methods have further enhanced RWE’s impact: rapid-cycle analyses of large clinical databases can produce actionable results in weeks, informing decisions on formulary tier placement or prior authorization criteria much faster than traditional studies. One health system, for instance, partnered with an analytics firm to evaluate an analgesic’s real-world effect on surgical recovery by analyzing thousands of patient cases alongside a 130-million-patient external data warehouse; this accelerated approach improved the depth of the formulary review while reducing the time and clinician effort required for pharmacy and therapeutics (P&T) committee evaluations.³

Real-world case studies illustrate how RWE is actively shaping formulary policy. In one example, the insurer Anthem analyzed claims outcomes for an oral asthma biologic versus standard inhalers in actual practice. The RWE revealed that when patients were nonadherent to inhaled therapy, those on the oral drug had fewer hospital visits and lower costs—information that prompted Anthem’s P&T committee to keep the oral agent on a preferred tier and to remove prior authorization requirements so that patients could continue accessing it without hurdles.⁴

In the realm of breakthrough biologics, payers are increasingly leveraging RWE early in a drug’s launch. For instance, pharmacy benefit managers (PBMs) have signaled they may require real-world data on a gene therapy’s durability and safety before granting broad formulary coverage, given the high upfront cost and limited long-term trial data. By analyzing initial patient outcomes, a PBM can confine coverage to subpopulations most likely to benefit and then expand access once real-world evidence affirms the therapy’s value.⁵

Likewise, RWE can refine utilization management over time. A recent claims-based study of glucagon-like peptide-1 (GLP-1) agonist biologics for obesity found only approximately 15% of patients remained on therapy after 2 years (versus approximately 85% adherence in clinical trials), highlighting a significant drop-off in real-world use.⁶ Such findings enable payers to adjust budget forecasts (anticipating that many patients discontinue early) and to revisit step therapy protocols or patient support programs to improve persistence. In all these cases, RWE has measurably impacted time to coverage decisions, financial planning, and patient access by providing data on how biologics perform beyond the controlled trial setting.

Employing RWE in formulary decision-making also raises key methodological considerations. Ensuring data validity is paramount; real-world datasets must be large and representative but also accurate and complete to inform high-stakes coverage choices. Observational comparisons are inherently prone to biases (eg, selection bias, confounding by indication), so payers and researchers prioritize bias mitigation strategies when interpreting RWE. Techniques such as propensity score matching are frequently used to balance baseline characteristics between patients receiving a new biologic and those on standard therapy, reducing confounding and approximating a randomized comparison.⁷ Sophisticated statistical methods further strengthen causal inferences: multivariable regression modeling adjusts for remaining measured covariates, and survival analysis (eg, Kaplan-Meier curves or Cox proportional hazards models) is applied to examine real-world outcomes like time to treatment failure or overall survival on a biologic. To prove the robustness of results, analysts routinely perform sensitivity analyses, testing whether changing assumptions (for instance, using alternative definitions of an outcome or addressing missing data) significantly alters the conclusions. These steps—careful study design, bias adjustment, and rigorous statistical testing—are essential to translate raw real-world data into reliable evidence that payers can trust. Indeed, although RWE studies lack the controlled randomization of trials, the use of such methods helps ensure that any observed advantage (or risk) of a biologic in the real world reflects a true clinical effect rather than underlying population differences.⁷

The growing integration of RWE into formulary decisions carries important implications for all stakeholders. Payers benefit from greater predictability and precision in budgeting: real-world utilization patterns and effectiveness data enable more accurate forecasting of a biologic’s impact on health care costs, and they provide justification for coverage or negotiation of value-based contracts. Armed with RWE, such as early signals of reduced hospitalizations or improved patient outcomes, payers can confidently place a biologic on a preferred tier or relax prior authorizations, knowing the decision is backed by outcomes data rather than hope—thereby avoiding both undue spending and under-treatment.⁶

Manufacturers of biologics also see faster uptake and speed to market when RWE is leveraged. By investing in postmarket studies or registry data, a manufacturer can demonstrate a drug’s real-world value to payers sooner, leading to quicker inclusion on formularies or fewer step therapy requirements. This accelerates patient access and differentiates the product in a crowded market while also informing physicians about the drug’s performance in broad populations. Meanwhile, patients ultimately gain more equitable access to life-saving biologics. Traditional trials often underrepresent elderly patients, racial and ethnic minorities, and those with comorbidities, but RWE encompasses these groups and highlights their outcomes.⁷ Therefore, coverage policies informed by RWE tend to be more inclusive and aligned with actual patient needs. For example, identifying which subpopulations truly benefit from a biologic ensures that prior authorization criteria are written to approve the drug for those patients without unnecessary delays.

In sum, the infusion of high-quality real-world evidence into formulary and benefit design is accelerating and refining decision-making for biologics. By complementing clinical trial data with insights from everyday practice, payers can align formulary tiering, prior authorization, and step therapy protocols more closely with real-world value, improving budget efficiency and expanding patient access to innovative therapies.

References

1. Brixner D, Biskupiak J, Oderda G, et al. Payer perceptions of the use of real-world evidence in oncology-based decision making. J Manag Care Spec Pharm. 2021;27(8):1096-1105. doi:10.18553/jmcp.2021.27.8.1096

2. Dayer VW, Drummond MF, Dabbous O, et al. Real-world evidence for coverage determination of treatments for rare diseases. Orphanet J Rare Dis. 2024;19(1):47. doi:10.1186/s13023-024-03041-z

3. Altshuler D, Yu K, Papadopoulos J, Dabestani A. Is P&T ready to add rapid cycle analytics to formulary? Hosp Pharm. 2021;56(5):430-435. doi:10.1177/0018578720918341

4. Pollock M, Cziraky M. Real-World Evidence Studies. Applied Clinical Trials. October 12, 2015. Accessed June 25, 2025. https://www.appliedclinicaltrialsonline.com/view/real-world-evidence-studies

5. Unlocking market access for gene therapies in the United States. McKinsey & Co. August 22, 2019. Accessed June 25, 2025. https://www.mckinsey.com/industries/life-sciences/our-insights/unlocking-market-access-for-gene-therapies-in-the-united-states

6. In the Real World, People Do Not Stick With GLP-1s for Weight Loss | AMCP Nexus 2024. Managed Healthcare Executive. October 18, 2024. Accessed June 25, 2025. https://www.managedhealthcareexecutive.com/view/in-the-real-world-people-do-not-stick-with-glp-1s-for-weight-loss-amcp-nexus-2024

7. van Nassau SCMW, Bol GM, van der Baan FH, et al. Harnessing the potential of real-world evidence in the treatment of colorectal cancer: where do we stand? Curr Treat Options Oncol. 2024;25(4):405-426. doi:10.1007/s11864-024-01186-4