超越各州:使用R开发离散事件模拟模型

相关实验视频

Updated: Jan 10, 2026

04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.7K

Ziyi Lin¹, Andrew Briggs²

¹Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK.

PharmacoEconomics

|November 28, 2025

概括

本指南展示了使用苏格兰心血管疾病 (CVD) 政策模型在R中构建离散事件模拟 (DES) 模型. 它有助于卫生经济学家从马尔科夫模型过渡到DES,以获得更准确的评估.

科学领域:

卫生经济学卫生经济学
计算建模计算建模
生物统计学生物统计学

背景情况:

苏格兰心血管疾病 (CVD) 政策模型,最初是一个复杂的基于Excel的马尔科夫模型,已经在R.
从既定的马尔科夫模型过渡到离散事件模拟 (DES) 对健康经济建模者来说是一个挑战.
复杂模型的公开可用的R代码为实际的DES实施培训提供了机会.

研究的目的:

为在R.中构建离散事件模拟 (DES) 模型提供一个逐步指南.
用苏格兰心血管疾病 (CVD) 政策模型作为案例研究来说明DES技术.
在健康经济评估中,为从业人员提供从马尔科夫到DES框架过渡的技能.

主要方法:

使用基本R函数和整洁verse包来实现透明和可重复的DES模型构建.
演示基于特定分布的时间到事件数据的模拟.
实施持续折扣并应对先进的挑战,如零碎成本和效用建模.

主要成果:

成功地将复杂的苏格兰CVD政策模型结构适应到R的DES框架中.
提供了明确的,可重复的方法,用于基本和高级的DES实现,而不需要专门的DES包.
展示了DES在处理复杂的健康经济建模场景中的实用性.

结论:

与传统的马尔科夫模型相比,离散事件模拟 (DES) 为健康经济评估提供了更高的准确性和灵活性.
这个基于R的示例为建模人员采用DES技术提供了一个实用的途径.
该方法促进了对DES在公共卫生政策建模中的更深入理解和应用.

相关概念视频

01:11

Introduction to R

4.1K

R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...

4.1K

01:29

Econometric Views (EViews)

531

Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...

531

01:16

Response Surface Methodology

583

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:

583

01:18

Introduction To Survival Analysis

714

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...

714

02:33

Exponential Equations for Modeling Growth

189

Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...

189