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Real-World Evidence: Bridging Gaps in Evidence to Guide Payer Decisions
Randomized controlled trials (RCTs) are preferred by payers for health technology assessments and coverage decisions. However, the inclusion of a highly selective patient population and the rigorously controlled conditions in RCTs may not be reflective of real-world clinical practice. Real-world evi...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895868/ https://www.ncbi.nlm.nih.gov/pubmed/32557235 http://dx.doi.org/10.1007/s41669-020-00221-y |
Sumario: | Randomized controlled trials (RCTs) are preferred by payers for health technology assessments and coverage decisions. However, the inclusion of a highly selective patient population and the rigorously controlled conditions in RCTs may not be reflective of real-world clinical practice. Real-world evidence (RWE) obtained from an analysis of real-world data (RWD) from observational studies can bridge gaps in evidence not addressed by RCTs and is thus valuable to public and private payers for decision-making. Through a broad literature search to obtain insights into payers’ experience, we found that payers have concerns about real-world studies with respect to data quality, poor internal validity, potential bias, and lack of meaningful endpoints. However, they valued RWE to fill evidence gaps not addressed by RCTs, such as high-quality, real-world, long-term effectiveness and safety data; head-to-head drug comparisons; cost analyses for tiering formulary placement; medication use and adherence patterns; identification of relevant responder and non-responder patient subpopulations; and patient-reported outcomes (PROs). RWE can be used to assess clinically meaningful endpoints and gauge the impact of interventions on the quality of healthcare. Here, we review how payers use or can use RWD on the comparative effectiveness and safety of treatments, PROs, medication adherence and persistence, prescribing patterns, healthcare resource utilization, and patient characteristics and/or biomarkers associated with treatment response when making health technology assessments and payer coverage decisions across therapeutic areas. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s41669-020-00221-y) contains supplementary material, which is available to authorized users. |
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