MPES-R: Multi-Parameter Evidence Synthesis in R for Survival Extrapolation—A Tutorial

Ash Bullement (), Mark Edmondson-Jones, Patricia Guyot, Nicky J. Welton, Gianluca Baio, Matthew Stevenson and Nicholas R. Latimer
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Ash Bullement: Sheffield Centre for Health and Related Research, University of Sheffield
Mark Edmondson-Jones: Delta Hat
Patricia Guyot: Sanofi
Nicky J. Welton: University of Bristol
Gianluca Baio: University College London
Matthew Stevenson: Sheffield Centre for Health and Related Research, University of Sheffield
Nicholas R. Latimer: Sheffield Centre for Health and Related Research, University of Sheffield

PharmacoEconomics, 2024, vol. 42, issue 12, No 2, 1317-1327

Abstract: 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.

Date: 2024
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DOI: 10.1007/s40273-024-01425-4

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