Reproducible analytical pipelines for healthcare discrete‑event simulation: An open guide and worked examples

Abstract

Discrete-event simulation (DES) is a popular technique for exploring problems in healthcare. For these models to be reused and have lasting impact, they need to be reproducible, transparent and well structured. Reproducible analytical pipeline (RAP) approaches are structured robust workflows that ensure analyses can be reproduced. They have emerged as best practice, but modellers struggle to implement them due to gaps in accessible guidance, skills, and time. This paper presents an integrated set of resources designed to help modellers bridge this implementation gap: an open-access e-book and four worked example repositories demonstrating complete RAP workflows for DES in both Python and R. The online book provides step-by-step guidance through nine major sections covering introductory material, project set-up, model inputs, model building, output analysis, experimentation, verification and validation, style and documentation, and collaboration and sharing. The case studies demonstrate varying complexity: a classic M/M/s queueing model, and a replication of a stroke care pathway model. The worked examples serve dual purposes: they demonstrate that RAP principles are achievable for healthcare DES models (from canonical queueing systems to real-world clinical pathways), and they provide templates that modelling teams can adopt and adapt within routine decision-support projects. ## Research artefacts

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