Related Experiment Video
Updated: May 7, 2026
20:24
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
Published on: January 31, 2014
16.3K
A Tutorial on Discrete Event Simulation Models Using a Cost-Effectiveness Analysis Example in R
Mauricio Lopez-Mendez1,2, Jeremy D Goldhaber-Fiebert1,3, Fernando Alarid-Escudero1,3
1Department of Health Policy, Stanford School of Medicine, Stanford University, Stanford, CA, USA.
Summary
This study introduces an open-source discrete event simulation (DES) framework for continuous-time individual-level state-transition models (iSTMs) in healthcare. The reliable DES framework enables more realistic modeling of health processes at lower costs.
Area of Science:
- Health economics and outcomes research
- Computational modeling and simulation
- Biostatistics
Background:
- Discrete event simulation (DES) is underutilized in healthcare decision-making due to implementation challenges.
- Existing methods often lack reliability and reproducibility for complex health processes.
Purpose of the Study:
- To develop an open-source DES framework for simulating individual-level state-transition models (iSTMs) in continuous time.
- To provide a reliable and accessible tool for modeling healthcare processes with greater realism.
Main Methods:
- Developed a modular DES framework using the next-reaction algorithm.
- Incorporated time-dependent transitions, age-dependent mortality, and treatment effects using validated sampling methods.
- Implemented the framework in R using the Sick-Sicker Model for demonstration.
Main Results:
- The framework successfully simulates individual state transitions in continuous time.
- Epidemiological outcomes, cost-effectiveness, and probabilistic analyses were obtained.
- Demonstrated the framework's ability to model realistic health dynamics.
Conclusions:
- The open-source DES framework offers a reliable and cost-effective alternative to discrete-time iSTMs.
- Enables more realistic simulation of health dynamics, improving medical decision-making.
- Facilitates advanced analyses such as cost-effectiveness and value-of-information.
Related Concept Videos
01:11Introduction to R
4.4K
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...
4.4K
01:25Modeling with Differential Equations
336
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
336
01:18Introduction To Survival Analysis
963
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...
The primary goal of survival analysis is to estimate survival time—the time...
963
01:29Econometric Views (EViews)
716
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...
716
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
442
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
442
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
360
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
360