Bullement, Ash;
Edmondson-Jones, Mark;
Guyot, Patricia;
Welton, Nicky J;
Baio, Gianluca;
Stevenson, Matthew;
Latimer, Nicholas R;
(2024)
MPES-R: Multi-Parameter Evidence Synthesis in R for Survival Extrapolation-A Tutorial.
PharmacoEconomics
10.1007/s40273-024-01425-4.
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Abstract
Survival extrapolation often plays an important role in health technology assessment (HTA), and there are a range of different approaches available. Approaches that can leverage external evidence (i.e. data or information collected outside the main data source of interest) may be helpful, given the extent of uncertainty often present when determining a suitable survival extrapolation. One of these methods is the multi-parameter evidence synthesis (MPES) approach, first proposed for use in HTA by Guyot et al., and more recently by Jackson. While MPES has potential benefits over conventional extrapolation approaches (such as simple or flexible parametric models), it is more computationally complex and requires use of specialist software. This tutorial presents an introduction to MPES for HTA, alongside a user-friendly, publicly available operationalisation of Guyot's original MPES that can be executed using the statistical software package R. Through two case studies, both Guyot's and Jackson's MPES approaches are explored, along with sensitivity analyses relevant to HTA. Finally, the discussion section of the tutorial details important considerations for analysts considering use of an MPES approach, along with potential further developments. MPES has not been used often in HTA, and so there are limited examples of how it has been used and perceived. However, this tutorial may aid future research efforts exploring the use of MPES further.
| Type: | Article |
|---|---|
| Title: | MPES-R: Multi-Parameter Evidence Synthesis in R for Survival Extrapolation-A Tutorial |
| Location: | New Zealand |
| DOI: | 10.1007/s40273-024-01425-4 |
| Publisher version: | http://dx.doi.org/10.1007/s40273-024-01425-4 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10196529 |
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