The increasing implementation of the R programming language in health technology assessments (HTAs) is the result of the need for transparency, reproducibility, and efficiency in health economic modelling. Unlike conventional excel-based tools, R provides a completely script-based setting that records every step of the modelling process, from data import to simulation and reporting. This improves auditability and error reduction while also enabling smooth automation, making R perfectly suitable to cater to the rising demand for “living HTAs” that evolve with new evidence.(1, 2)

HTA bodies, including NICE (the UK) and ZIN (the Netherlands) are adopting R-based submissions, indicating rising institutional confidence in open-source, code-driven methodologies.(3) The ability of R programming manage multifaceted systems, incorporate version control, and automate analyses is changing how HTAs are performed. Academic and industry partnerships are creating shared frameworks and toolkits to further simplify these processes, facilitating consistent, transparent, and faster decision-making.(1, 3)

Standardisation is the most crucial factor of R adoption. For this, validated and reusable modelling frameworks are being developed to help regulators.(4) Initiatives like the open-source assertHE package integrate validation and quality checks right into modelling workflows, supporting built-in verification rather than retrospective review.(5) These frameworks reduce review time, enhance reproducibility, and facilitate efficient model adaptation across markets, thus striking a balance between innovation and rigour. The growing number of health economists equipped with R expertise further reinforces this ecosystem, shifting toward code-based submissions that are easier to review, update, and share.(1, 4, 5)

The move toward standardisation also facilitates scalability in global HTAs. R also facilitates country-specific modifications through modular inputs rather than structural model changes, maintaining consistency across jurisdictions. Shared code sources, scenario templates, and uniform data structures are helpful in cross-country comparisons, making them more reliable and less resource-intensive.(1, 3, 4)

R’s versatility goes beyond modelling efficiency to support real-world data (RWD) and artificial intelligence (AI) integration, which are the crucial pillars of HTA evidence bases. R’s capacity for secure data handling, API-based automation, and remote computation enables models to advance dynamically while maintaining data privacy. However, challenges, especially about data quality, interoperability, and achieving methodological agreement across agencies, persist. Overcoming these warrants collective participation from academia, regulators, and industry to establish shared standards and training guidance.(2, 3, 6)

Finally, the implementation and standardisation of R signify a critical step towards making HTAs more transparent, reproducible, and globally aligned. By adopting open-source technology and collaborative validation, R is transforming the assessment of health technologies, taking HTA from static assessments into dynamic, data-driven systems that adapt to evidence and policy needs of the real-world.

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References

1. Smith RA, Schneider PP, Mohammed W. Living HTA: Automating Health Economic Evaluation with R. Wellcome Open Res. 2022; 11(7):194.
2. R Consortium. R for Health Technology Assessment (HTA): Identifying Needs, Streamlining Processes, Building Bridges. Accessed online on 10^th November 2025. Available at: https://r-consortium.org/posts/r-for-health-technology-assessment-hta-identifying-needs-streamlining-processes-building-bridges/
3. Poerrier JE, Ettinger J, Bergemann R. R in HEOR modelling for HTA submissions: An assessment. Accessed online on 10^th November 2025. Available at: https://www.parexel.com/application/files/2917/2729/8142/FY24_R_in_HEOR_Modelling_White_Paper_09-2024_v3.pdf
4. Thokala, P., Srivastava, T., Smith, R. et al. Living Health Technology Assessment: Issues, Challenges and Opportunities. PharmacoEconomics. 2023; 41:227–237.
5. Smith RA, Samyshkin Y, Mohammed W, et al. assertHE: an R package to improve quality assurance of HTA models. [version 1; peer review: 1 approved, 1 approved with reservations]. Wellcome Open Res. 2024; 9:701.
6. Zisis K, Pavi E, Geitona M, Athanasakis K. Real-world data: a comprehensive literature review on the barriers, challenges, and opportunities associated with their inclusion in the health technology assessment process. J. Pharm. Pharm. Sci. 2024; 27:12302.