Author: Javier Mar Medina | Ad Honorem Researcher, Biogipuzkoa Health Research Institute
Real-world data (RWD), collected from routine clinical practice, generate real-world evidence (RWE) that supports decision-making by regulatory agencies, health technology assessment (HTA) bodies, and healthcare payers (1). To maximize the value of RWD in decision-making, a range of analytical methods is employed to reduce the biases inherent to its observational nature (2). The advantages of RWD include its availability, generalizability, and its ability to capture treatment patterns as they occur in routine clinical practice (3). However, these advantages must be weighed against challenges related to data quality, including missing data, inaccuracies, and inconsistencies in the recording and definition of participant characteristics (3). In the regulatory context, the adoption of RWD in economic evaluation has been slow due to concerns regarding data quality and uncertainty about the most appropriate analytical methods (4). Although its use for estimating treatment effectiveness has been widely recognized by leading clinical journals (3), its incorporation into market access processes has progressed at a much slower pace (4). Furthermore, there is currently no consensus on what exactly constitutes a cost-effectiveness model based on RWD (5).
Cost-effectiveness analysis (CEA) is a type of economic evaluation that compares the costs and outcomes of two or more interventions with different levels of effectiveness (6). Existing examples generally rely on conventional state-transition models parameterized using RWD (7,8). Restricting the analysis to this approach, however, fails to exploit one of the principal strengths of RWD: the availability of patient-level information for the target population. Discrete-event simulation (DES) models in HTA (9,10) enable the use of individual-level data but have seen limited adoption because of their greater methodological complexity and steeper learning curve. Combining DES with RWD, however, has the potential to facilitate its implementation and to promote the development of robust and transparent methods for RWD-based economic evaluation. Previous proposals advocating the use of RWD in economic evaluation have highlighted its potential value for HTA but have not provided practical methods or procedures for implementation (11), or have been limited to considering RWD solely as a source of model parameters (7,8). The dissemination of methods that simplify implementation will help expand the use of RWD in the economic evaluation of health technologies. In this context, DES provides a framework for integrating RWD datasets, creating synergies that streamline the modelling process (9,10). DES leverages patient-level data by simulating individual entities representative of the target population; however, its technical complexity has limited its widespread adoption (9). Conducting a DES-based research study also requires the integration of multiple methodological techniques, creating a significant barrier to entry for new users.
Advances in DES modelling have been facilitated by the increasing availability of technical resources and survival analysis tools. The FORECAST (FOrward REsearch on Clinical and Survival Trends) application, available through the GitHub repository (https://fwdcast.github.io/), analyses a dataset and identifies the best-fitting parametric survival function (12,13). Standardized tools are essential to promote the rigorous and transparent use of RWD in economic evaluations (14,15).
Model-based economic evaluation has traditionally required substantial methodological effort because it integrates knowledge from multiple disciplines. Its combination with RWD databases creates an opportunity to capitalize on the universal healthcare coverage provided in Spain under a Beveridge healthcare system. In contrast to Bismarck-type healthcare systems, the universal coverage of the Beveridge model means that electronic health record databases include virtually the entire population. Consequently, cost-effectiveness studies can be conducted by analysing all individuals diagnosed with a specific disease and the different treatments they have received. By preserving patient-level characteristics, it is possible to maintain the correlation structure between variables, thereby producing more robust analyses.
References
1. Bowrin K, Briere J-B, Levy P, et al. Cost-effectiveness analyses using real-world data: an overview of the literature. J Med Econ. junio de 2019;22(6):545-53.
2. Garrison LP, Neumann PJ, Erickson P, et al. Using real-world data for coverage and payment decisions: the ISPOR Real-World Data Task Force report. Value Health. octubre de 2007;10(5):326-35.
3. Hubbard RA, Gatsonis CA, Hogan JW, et al. «Target Trial Emulation» for Observational Studies – Potential and Pitfalls. N Engl J Med. 28 de noviembre de 2024;391(21):1975-7.
4. Claire R, Elvidge J, Hanif S, et al. Advancing the use of real world evidence in health technology assessment: insights from a multi-stakeholder workshop. Front Pharmacol. 2024;14:1289365.
5. Bowrin K, Briere J-B, Levy P, et al. Use of real-world evidence in meta-analyses and cost-effectiveness models. J Med Econ. octubre de 2020;23(10):1053-60.
6. Drummond MF, Sculpher MJ, Claxton K, et al. Methods for the Economic Evaluation of Health Care Programmes. 4th edition. Oxford, United Kingdom ; New York, NY, USA: Oxford University Press; 2015. 464 p.
7. Bowrin K, Briere J-B, Fauchier L, et al. Real-world cost-effectiveness of rivaroxaban compared with vitamin K antagonists in the context of stroke prevention in atrial fibrillation in France. PLoS One. 2020;15(1):e0225301.
8. Escobar Cervantes C, Martí-Almor J, Cabeza AIP, et al. Real-world cost-effectiveness analysis of NOACs versus VKA for stroke prevention in Spain. PLoS One. 2022;17(4):e0266658.
9. Caro JJ, Möller J, Karnon J, et al. Discrete Event Simulation for Health Technology Assessment. Boca Raton: CRC Press; 2015. 374 p.
10. Karnon J, Stahl J, Brennan A, et al. Modeling using discrete event simulation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-4. Med Decis Making. octubre de 2012;32(5):701-11.
11. Muñoz Fernández C, Prieto Remón L, García Armesto S. Programme for the Use of Real World Data in Health Technology Assessment. Potential uses for Real World Data (RWD) in the Spanish HTA Network. [Internet]. Zaragoza: Aragon Health Sciences Institute (IACS); 2024. (Line of methodological developments of the Spanish Network of Health Technology Assessment Agenciesand National Health System Services: IACS). Disponible en: https://www.iacs.es/wp-content/uploads/2024/05/00_IACS_DVR_E2_DEF_NIPO_EN.pdf
12. Epstein D. ¿Extrapolación en la evaluación económica? ¡Please, please, help me! [Internet]. Economía y salud AES. 2025. Disponible en: https://www.aes.es/blog/2025/04/30/extrapolacion-en-la-evaluacion-economica-please-please-help-me/
13. Epstein D, Pérez Troncoso D. FORECAST (FOrward REsearch on Clinical And Survival Trends) [Internet]. 2025 [citado 15 de junio de 2025]. Disponible en: https://fwdcast.github.io/
14. Kent S, Burn E, Dawoud D, et al. Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment. Pharmacoeconomics. marzo de 2021;39(3):275-85.
15. Latimer NR. Survival analysis for economic evaluations alongside clinical trials–extrapolation with patient-level data: inconsistencies, limitations, and a practical guide. Med Decis Making. agosto de 2013;33(6):743-54.
