This is the repository for the rcea package, which accompanies a short course on model-based cost-effectiveness analysis (CEA) with R. A range of models are covered including time-homogeneous and time-inhomogeneous Markov cohort models, partitioned survival models, and semi-Markov individual patient simulations.

R for Health Technology Assessment (HTA) is an academic consortium whose main objective is to explore the use of R for cost-effectiveness analysis (CEA) as an alternative to less efficient, generalisable and powerful software such as spreadsheets.

heemod 程序包主要开发用于卫生经济学评价，包括Markov 模型的构建、运行和敏感性分析等；与Excel 和TreeAge 等卫生经济学评价的常用软件相比，基于R 进行卫生经济学研究具有开源免费、灵活性高和图形功能强大等优势。

This is the repository for the rcea package, which accompanies a short course on model-based cost-effectiveness analysis (CEA) with R. A range of models are covered including time-homogeneous and time-inhomogeneous Markov cohort models, partitioned survival models, and semi-Markov individual patient simulations.

Abstract We describe in detail how to perform health economic cost-effectiveness analyses (CEA) using the R package BCEA (Bayesian Cost-Effectiveness Analysis). CEA consist of analytic approaches for combining costs and health consequences of intervention(s).

The most commonly used model for cost-effectiveness analysis (CEA) is the cohort discrete time state transition model (cDTSTM), commonly referred to as a Markov cohort model. In this tutorial we demonstrate implementation with R of the simplest of cDSTMs, a time-homogeneous model with transition probabilities that are constant over time.

Carotid endarterectomy is a surgical procedure used to reduce the risk of stroke from carotid artery stenosis (narrowing the internal carotid artery). In an endarterectomy, the surgeon opens the artery and removes the plaque.

2025年1月21日 · The objective of this workshop is to explore the use of R for CEA as an alternative to less efficient, generalisable and powerful statistical software. General topics of interest include a wide range of technical aspects, e.g. the discussion of the many available R add-on packages, as well as ways to help users get the most out of R for CEA.

In this analysis we will perform a CEA given the output of model from the “Semi-Markov Multi-state Model” tutorial. We will use the CEA functions from hesim to summarize decision uncertainty and ggplot2 for visualization.

CEA in Rusing continuous-time multi-state modelling Dr Claire Williams R for CEA Workshop, UCL 11th July 2018 1