(Ebook) Discrete Event Simulation for Health Technology Assessment 1st Edition by J Jaime Caro, Jörgen Möller, Jonathan Karnon, James Stahl, Jack Ishak ISBN 9780429173295 0429173296

(Ebook) Discrete Event Simulation for Health Technology Assessment 1st Edition by J Jaime Caro, Jörgen Möller, Jonathan Karnon, James Stahl, Jack Ishak ISBN 9780429173295 0429173296

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ISBN 10: 0429173296
ISBN 13: 9780429173295
Author: J Jaime Caro, Jörgen Möller, Jonathan Karnon, James Stahl, Jack Ishak

(Ebook) Discrete Event Simulation for Health Technology Assessment 1st Edition Table of contents:

1 Introduction

1.1 HTA Context

1.2 What Is Discrete Event Simulation?

1.3 How Does DES Compare to Other Techniques Commonly Used in HTA?

1.4 When Is Discrete Event Simulation Useful?

1.5 Acceptance of Discrete Event Simulation

1.5.1 Stochastic Behavior

1.5.2 Data Needs

1.5.3 Execution Speed

1.5.4 Law of the Instrument (Wikipedia Contributors 2014)

1.5.5 Transparency

2 Central Concepts

2.1 Events

2.1.1 Types of Events

2.1.2 Event Consequences

2.2 Event Occurrence

2.2.1 Time-to-Event Approach

2.2.2 Periodic Checking

2.2.3 Event Ordering

2.3 Entities

2.3.1 Why Individuals?

2.3.2 Types of Entities

2.3.3 Creation and Removal of Entities

2.4 Attributes

2.4.1 Attribute Values

2.4.2 Types of Attributes

2.4.3 What Information Should Be Stored as an Attribute?

2.4.4 Memory

2.5 Time

2.6 Resources and Queues

2.7 Global Information

2.8 Distributions

2.8.1 Specifying a Distribution

2.8.2 Commonly Used Distributions

2.9 Using Influence Diagrams

3 Implementation

3.1 Control Logic

3.1.1 Branching

3.2 Using Distributions

3.2.1 Selecting a Distribution

3.2.2 Incorporating the Distribution

3.2.3 Selecting Values from a Distribution

3.2.3.1 Assigning Sociodemographic Characteristics and Disease Status

3.2.3.2 Sampling Time to an Event

3.3 Event Handling

3.3.1 Event Calendar

3.3.2 Entity-Level Event Lists

3.4 Specifying Events

3.4.1 Use of Submodels

3.4.2 Monitoring and Treatment Change Events

3.4.3 Specialized Modeling Events

3.4.3.1 Events to Assign Values

3.4.3.2 Events to Prompt Input and Output

3.4.3.3 Events to End the Simulation

3.4.4 Event Sequences

3.4.5 Composite Events

3.4.6 Duration of Events

3.5 Creating the Population

3.5.1 Dynamic Creation

3.5.2 Using Super-Entities

3.5.3 Creating Other Entity Types

3.5.4 Agents

3.6 Assigning Attributes

3.6.1 Using Summary Characteristics

3.6.2 Using Real People

3.6.3 Duplication of Entities

3.6.4 Updating Values and Retaining History

3.6.5 Handling Quality of Life

3.6.6 Other Values

3.7 Handling Time

3.7.1 Advancing the Clock Using Time-to-Event

3.7.2 Advancing the Clock in Fixed Steps

3.7.3 Recording Times

3.7.4 Matching Actual Time

3.7.5 Delaying Time Zero

3.8 Applying Intervention Effects

3.8.1 Effects on Event Times

3.8.1.1 Using Kaplan–Meier Curves

3.8.1.2 Using Summary Measures

3.8.1.3 Dealing with Composite Outcomes

3.8.2 Changes in a Level

3.8.3 Unintended Effects

3.9 Recording Information

3.9.1 What to Record

3.9.2 How to Record

3.9.3 Discounting

3.10 Reflecting Resource Use

3.10.1 Implicit Handling Using Cost Accumulators

3.10.2 Explicit Representation

3.10.2.1 Capacity

3.10.2.2 Scheduling

3.10.2.3 Utilization

3.10.2.4 Queues

4 A Simple Example

4.1 Design

4.1.1 Problem

4.1.2 Influence Diagram

4.1.3 Model Technique

4.1.4 Flow Diagram

4.2 Obtaining the Inputs

4.2.1 Patient Characteristics, Costs, and Quality of Life

4.2.2 Equations

4.3 Structuring the Model

4.3.1 Creating the Entities

4.3.2 Assigning Initial Attribute Values

4.3.3 Assigning Recurrence Event Time

4.3.3.1 For Standard of Care

4.3.3.2 Applying the Treatment Effect

4.3.4 Accruing Values Until Recurrence

4.3.5 Updating Attribute Values

4.3.6 Assigning the Time of Death

4.3.7 Accruals at the Second Event

4.4 Obtaining Results

5 Analyses

5.1 Base Case

5.1.1 Dealing with Chance (Stochastic Uncertainty)

5.1.2 Reflecting Uncertainty in Parameter Inputs

5.1.2.1 Deterministic Uncertainty Analyses

5.1.2.2 Probabilistic Uncertainty Analyses

5.1.3 Structural Uncertainty Analyses

5.2 Exploring Sensitivity to Input Values

5.2.1 Subgroups

5.2.2 Other Sensitivities of Interest

5.2.3 Threshold Values

6 Formulating the Required Equations

6.1 Requirements for the Equations

6.2 Selecting Data Sources

6.2.1 Interventional Data Sources

6.2.2 Observational Data Sources

6.2.2.1 Routinely Collected Data

6.2.2.2 Prospectively Collected Data

6.2.3 Considerations When Using Multiple Sources of Data

6.3 Taxonomy of Equation Types Commonly Used in DES

6.3.1 GLM-Based Equations

6.3.1.1 Continuous Response Variables

6.3.1.2 Categorical Data

6.3.1.3 Count Data

6.3.2 Handling Longitudinal Data

6.3.3 Time-to-Event Analyses

6.3.3.1 Deriving Equations

6.3.3.2 Incorporating Time-Dependent Predictors

6.4 Selection of Predictors

6.4.1 Role of Predictors in the Equation

6.4.2 Building the Equation

6.5 Validation of the Final Equation

6.6 Combining Inputs and Equations from Different Sources

6.6.1 Incorporating External Data on Intervention Effects

6.6.1.1 Considerations with Comparative Data on External Treatments

6.6.1.2 Considerations for Non-Comparative Data on Treatment Effects

6.6.2 Joining Time-to-Event Distributions and Predictive Equations

7 Efficiency and Variance Reduction

7.1 Reducing Unwanted Variance

7.1.1 Duplicating Entities

7.1.2 Duplication of Pathways

7.1.3 Synchronizing Entities

7.2 Other Efficiency Improvements

7.2.1 Time-to-Event Approach Instead of Periodic Checking

7.2.2 Simultaneous Inclusion of Entities

7.2.3 Using Super Entities

7.3 Settings Affecting Model Execution

7.3.1 Animation Settings

7.3.2 Speed-Destroying Leftovers

8 Validation

8.1 Face Validity

8.1.1 Problem Specification and Model Conceptualization

8.1.2 Is the Choice to Use DES Justified?

8.1.3 Is the Specification of Events Adequate?

8.1.4 How Will Time Be Advanced?

8.1.5 What Entities Will Be Simulated?

8.1.6 What Attributes Will Be Modeled?

8.2 Verification

8.2.1 Model Logic and Implementation

8.2.2 Equations

8.2.3 Code

8.3 External Validation

8.3.1 Dependent Validation

8.3.2 Independent Validation

8.3.3 Predictive Validation

9 Special Topics

9.1 Documentation

9.1.1 Design Stage

9.1.2 Input Data Stage

9.1.3 Implementation Stage

9.1.4 Analysis Stage

9.1.5 Validation

9.1.6 Reports

9.1.6.1 Non-Technical Documentation

9.1.6.2 Technical Report

9.2 Animation

9.2.1 Purpose of Animation

9.2.2 Difficulties with Animation

9.2.3 Components

9.2.4 How to Animate

9.3 Software

9.3.1 DES Software History

9.3.2 DES Software Future

9.3.3 Specialized for DES

9.3.4 General Programming Languages

9.3.5 Spreadsheets

9.3.6 Spreadsheets Plus

9.3.6.1 Visual Basic for Applications

9.3.6.2 Monte-Carlo-Simulation Extensions

9.3.7 TreeAge

9.3.8 R

9.4 Agent-Based Models

9.5 Hybrid Models

9.5.1 With Continuous Simulation

9.5.2 With State-Transition Models

9.5.3 With System Dynamic Models

9.5.4 With Decision Trees

10 Case Study: Breast Cancer Surveillance

10.1 Background

10.2 Why DES?

10.3 Design

10.3.1 Population

10.3.2 Structure

10.3.3 Interventions

10.4 Data Sources

10.5 Implementation

10.6 Analyses

10.7 Results

10.8 Comments

References

Index

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Tags: J Jaime Caro, Jörgen Möller, Jonathan Karnon, James Stahl, Jack Ishak, Event Simulation, Health Technology Assessment