@misc{10481/84693,
year = {2023},
month = {7},
url = {https://hdl.handle.net/10481/84693},
abstract = {Background

INES (INteractive model for Extrapolation of Survival and cost) provides an open-access tool powered by R that implements three-state partitioned survival models (PSM). This article describes the properties of the tool, and the situations where INES may or may not be suitable.
Methods

INES is designed to be used by investigators or healthcare professionals who have a good grasp of the principles of economic evaluation and understand the strengths and weaknesses of partitioned survival models, but are not sufficiently familiar with a statistical package such as Excel or R to be able to construct and test a de-novo PSM themselves.

INES is delivered to the user via a batch file. Once downloaded to the user’s hard drive, it interacts with the user via a portable version of R with web interactivity built in Shiny. INES requires absolutely no knowledge of R and the user does not need to have R or any of its dependences installed. Hence the user will deal with a standalone Shiny app. Inputs (digitalized survival curves, unit costs, posology, hazard ratios, discount rate) can be uploaded from a template spreadsheet.
Results

The INES application provides a seamlessly integrated package for estimating a set of parametric hazard functions for progression free and overall survival, selecting an appropriate function from this menu, and applying this as an input to a PSM to calculate mean costs and quality-adjusted life years. Examples are given that may serve as a tutorial.
Conclusion

INES offers a rapid, flexible, robust and transparent tool for parametric survival analysis and calculating a PSM that can be used in many different contexts.},
publisher = {BMC},
keywords = {Partitioned survival model},
keywords = {Cost-effectiveness analysis},
keywords = {Economic evaluation},
keywords = {Extrapolation},
keywords = {Survival analysis},
title = {INES: Interactive tool for construction and extrapolation of partitioned survival models},
doi = {10.1186/s12962-023-00456-6},
author = {Gimeno-Ballester, Vicente and Pérez Troncoso, Daniel and Olry‑Labry, Antonio and Epstein, David Mark},
}