A Toolkit for Health-economic Analysis with Language-Agnostic Simulation and Sensitivity Analysis (THALASSA)
- 1Catalan Institute of Oncology - IDIBELL
Abstract
BackgroundCost-effectiveness analyses (CEA) built on simulation models are usually delivered through project-specific scripts. Each new model, and often each new question, rebuilds the same scaffolding: parameter management, calibration, base-case analysis, sensitivity analysis and export. This is costly, and is where traceability between an estimate and the model version producing it is lost. We present THALASSA, a web application providing this infrastructure once, for any model satisfying a small programming contract.
MethodsA simulation model is connected by one file implementing a fixed contract of R functions that determine its behaviour. THALASSA exposes the complete CEA workflow in a browser: a generated overview of the connected model, parameter editing, calibration, base-case comparison with the efficiency curve, and deterministic and probabilistic sensitivity analysis at a willingness-to-pay threshold, each with timestamped file export carrying the parameter values it was run with. Every session is bound to a repository version, so results carry their generating model version.
ResultsWe demonstrate THALASSA on two public models of contrasting difficulty: a simple tutorial model and a generic cancer screening model with two competing natural-history pathways and a 21-dimensional calibration deliberately built to be more complex. Both ran on the same unmodified setup, implementing the same contract, with no code changes.
ConclusionsSeparating analysis infrastructure from model logic makes the CEA workflow reusable across projects and reproducible by construction. A modelling team can move from a versioned repository to audit-ready, exportable CEA outputs without writing analysis code. The integration effort does not grow with model size and one installation serves unrelated projects.
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BibTeX
@misc{gomezguillen2026toolkit,
title = {{A Toolkit for Health-economic Analysis with Language-Agnostic Simulation and Sensitivity Analysis (THALASSA)}},
author = {D. Gomez-Guillen and D. Fernandez-Lopez and J. Cerquides and J. L. Arcos and M. Diaz},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.17.26365802},
url = {https://www.medrxiv.org/content/10.64898/2026.10.17.26365802v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - A Toolkit for Health-economic Analysis with Language-Agnostic Simulation and Sensitivity Analysis (THALASSA)
AU - D. Gomez-Guillen
AU - D. Fernandez-Lopez
AU - J. Cerquides
AU - J. L. Arcos
AU - M. Diaz
PY - 2026
DA - 2026/10/17
PB - medRxiv
DO - 10.64898/2026.10.17.26365802
UR - https://www.medrxiv.org/content/10.64898/2026.10.17.26365802v1
AB - BackgroundCost-effectiveness analyses (CEA) built on simulation models are usually delivered through project-specific scripts. Each new model, and often each new question, rebuilds the same scaffolding: parameter management, calibration, base-case analysis, sensitivity analysis and export. This is costly, and is where traceability between an estimate and the model version producing it is lost. We present THALASSA, a web application providing this infrastructure once, for any model satisfying a small programming contract. MethodsA simulation model is connected by one file implementing a fixed contract of R functions that determine its behaviour. THALASSA exposes the complete CEA workflow in a browser: a generated overview of the connected model, parameter editing, calibration, base-case comparison with the efficiency curve, and deterministic and probabilistic sensitivity analysis at a willingness-to-pay threshold, each with timestamped file export carrying the parameter values it was run with. Every session is bound to a repository version, so results carry their generating model version. ResultsWe demonstrate THALASSA on two public models of contrasting difficulty: a simple tutorial model and a generic cancer screening model with two competing natural-history pathways and a 21-dimensional calibration deliberately built to be more complex. Both ran on the same unmodified setup, implementing the same contract, with no code changes. ConclusionsSeparating analysis infrastructure from model logic makes the CEA workflow reusable across projects and reproducible by construction. A modelling team can move from a versioned repository to audit-ready, exportable CEA outputs without writing analysis code. The integration effort does not grow with model size and one installation serves unrelated projects.
ER -