Best practice RWE approaches to support economic modelling for HTA

Pharma Market Access Insights - from Mtech Access

How can real-world evidence (RWE) support health technology assessment (HTA)? Can real-world data (RWD) supplement clinical data? How can RWE be used to solve common challenges with treatment comparison?
Here, Mtech Access are joined by experts from Arcturis and Delta Hat. Dan Howard (Associate Director – Health Economics, Mtech Access) shares some of the challenges that our clients face when developing HTA-ready health economic models with limited clinical trial data. Joseph O’Reilly (Principal Medical Statistician, Arcturis) introduces solutions to these challenges using RWD and RWE approaches. Nick Latimer (Analyst, Delta Hat; Professor of Health Economics, University of Sheffield; former NICE Appraisal Committee member) discusses how RWE is assessed by HTA committees. Samantha Gillard (Director – HTA, Mtech Access) facilitates the discussion and puts your questions to our experts
This episode was first broadcast as a live webinar in October 2024. To request a copy of the slides used or to learn more visit: https://mtechaccess.co.uk/rwe-approaches-economic-modelling-hta/
For support with real-world evidence analysis, health economic modelling of health technology assessment, email [e-mail] or visit https://mtechaccess.co.uk/

Subscribe to our newsletter to hear more news, insights and events from Mtech Access.

Luvut

1. Welcome and introductions (00:00:00)

2. Partitioned survival modelling for HTA and common challenges (00:02:48)

3. What is partitioned survival modelling (00:03:08)

4. Visualising partitioned survival modelling and estimating health state membership (00:04:02)

5. Universal issues in oncology HTA modelling (00:06:09)

6. Types of evidence used in oncology modelling (00:10:31)

7. What is real-world data? (00:15:37)

8. Real-world data to generate an external control arm (00:17:21)

9. What is an ECA? (00:17:49)

10. ECAs can fill key HTA evidence gaps: Clinical effectiveness (00:20:12)

11. ECA case study: ZUMA-5 versus SCHOLAR-5 (00:21:48)

12. Comparator use and associated costs & ICER influencing model assumptions (00:28:56)

13. The HTA reviewers perspective (00:30:06)

14. Can HTA bodies accept Real-world data and evidence? (00:31:09)

15. Case study - what the NICE committee said (00:41:10)

16. Conclusions (00:44:11)

17. Reflections and next steps (00:46:53)

18. Q&A - Application to non oncology settings (00:47:42)

19. Q&A - Use of RWE in the case of missing comparative data in hard to research areas (00:48:02)

20. Q&A - Unmet opportunities for HTA agencies with RWE (00:50:35)

21. Q&A - RWE changing HTA committee's decision making (00:52:37)

22. Q&A - How can RWE contribute to sort of long term monitoring of health technologies post-approval? (00:53:53)

23. Q&A - What role can machine learning or AI play when we're generating RWE to support HTA processes? (00:55:42)