Discrete Event Simulation for Health Technology Assessment: 1st Edition (Hardback) book cover

Discrete Event Simulation for Health Technology Assessment

1st Edition

By J. Jaime Caro, Jörgen Möller, Jonathan Karnon, James Stahl, Jack Ishak

Chapman and Hall/CRC

354 pages | 117 B/W Illus.

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pub: 2015-10-16
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Description

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.

Table of Contents

Introduction

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

Central Concepts

Events

Event Occurrence

Entities

Attributes

Time

Resources and Queues

Global Information

Distributions

Using Influence Diagrams

Implementation

Control Logic

Using Distributions

Event Handling

Specifying Events

Creating the Population

Assigning Attributes

Handling Time

Applying Intervention Effects

Recording Information

Reflecting Resource Use

A Simple Example

Design

Obtaining the Inputs

Structuring the Model

Obtaining Results

Analyses

Base Case

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

Validation

Face Validity

Verification

External Validation

Special Topics

Documentation

Animation

Software

Agent-Based Models

Hybrid Models

Case Study: Breast Cancer Surveillance

Background

Why DES?

Design

Data Sources

Implementation

Analyses

Results

Comments

About the Authors

J. Jaime Caro, MDCM, FRCPC, FACP, is an adjunct professor of medicine as well as epidemiology and biostatistics at McGill University. He also teaches discrete event simulation (DES) at Thomas Jefferson University School of Population Health and is chief scientist at Evidera. He founded the Caro Research Institute, chaired the Modeling Task Force jointly sponsored by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the Society for Medical Decision Making (SMDM), chaired the Expert Panel guiding the German government on methods for health technology assessment (HTA), and helped the World Bank Institute address the growing problem of supreme courts overriding health care system decisions.

Jörgen Möller, MSc Mech Eng, is an associate researcher in the Division of Health Economics, Faculty of Medicine at Lund University and vice president of modeling technology at Evidera. He is an expert in health care and logistics management decision modeling using advanced techniques. His work focuses on translating methods from operations research to pharmacoeconomics, developing guidelines for this type of modeling, and conducting advanced courses in DES and Arena software.

Jonathan Karnon, PhD, is a professor in health economics at the University of Adelaide. He wrote one of the few papers that directly compared cohort state transition models and DES techniques for HTA, built a range of DES HTA models for the evaluation of alternative screening strategies, and co-chaired the ISPOR/SMDM modeling good research practices taskforce working group on the use of DES for HTA.

James E. Stahl, MDCM, MPH, is an associate professor of medicine in the Geisel School of Medicine at Dartmouth College, Section Chief of General Internal Medicine at Dartmouth-Hitchcock Medical Center, the director of systems engineering for the Point of Care Testing Research Network at the Center for Integration of Medicine and Innovative Technology, senior scientist at the MGH Institute for Technology Assessment, and adjunct professor in mechanical and industrial engineering at Northeastern University. His work focuses on health care delivery, process redesign, the development and evaluation of innovation in health care, and improvement in patient experience.

K. Jack Ishak, PhD, is executive director of biostatistics and senior research scientist at Evidera. Dr. Ishak specializes in statistical methods for health economics, pharmaco-epidemiology, and observational research. His current methodological work focuses on study designs for comparative effectiveness research (such as pragmatic and Bayesian adaptive trial designs), methods for adjusting for bias due to crossover in oncology trials, and simulation-based techniques for treatment comparisons (including trial simulation).

Subject Categories

BISAC Subject Codes/Headings:
BUS049000
BUSINESS & ECONOMICS / Operations Research
BUS070080
BUSINESS & ECONOMICS / Industries / Service Industries
MAT029000
MATHEMATICS / Probability & Statistics / General
MED002000
MEDICAL / Administration