Content: 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