Chapman and Hall/CRC
354 pages | 117 B/W Illus.
Discover How to Apply DES to Problems Encountered in HTA
Discrete event simulation (DES) has traditionally been used in the engineering and operations research fields. The use of DES to inform decisions about health technologies is still in its infancy. Written by specialists at the forefront of this area, Discrete Event Simulation for Health Technology Assessment is the first book to make all the central concepts of DES relevant for health technology assessment (HTA). Accessible to beginners, the book requires no prerequisites and describes the concepts with as little jargon as possible.
The book first covers the essential concepts and their implementation. It next provides a fully worked out example using both a widely available spreadsheet program (Microsoft Excel) and a popular specialized simulation package (Arena). It then presents approaches to analyze the simulations, including the treatment of uncertainty; tackles the development of the required equations; explains the techniques to verify that the models are as efficient as possible; and explores the indispensable topic of validation. The book also covers a variety of non-essential yet handy topics, such as the animation of a simulation and extensions of DES, and incorporates a real case study involving screening strategies for breast cancer surveillance.
This book guides you in leveraging DES in your assessments of health technologies. After reading the chapters in sequence, you will be able to construct a realistic model designed to help in the assessment of a new health technology.
The HTA Context
What Is Discrete Event Simulation?
How Does DES Compare to Other Techniques Commonly Used in HTA?
When Is Discrete Event Simulation Useful?
Acceptance of Discrete Event Simulation
Resources and Queues
Using Influence Diagrams
Creating the Population
Applying Intervention Effects
Reflecting Resource Use
A Simple Example
Obtaining the Inputs
Structuring the Model
Exploring Sensitivity to Input Values
Formulating the Required Equations
Requirements for the Equations
Selecting Data Sources
Taxonomy of Equation Types Commonly Used in DES
Selection of Predictors
Validation of the Final Equation
Combining Inputs and Equations from Different Sources
Efficiency and Variance Reduction
Reducing Unwanted Variance
Other Efficiency Improvements
Settings Affecting Model Execution
Case Study: Breast Cancer Surveillance