Risk Analysis Foundations, Models, and Methods: Foundations, Models and Methods

1: Introduction and Basic Risk Models. 1: Introduction. 1.1. Distinguishing Characteristics Of Risk Analysis. 1.2. The Traditional Health Risk Analysis Framework. 1.3. Defining Risks: Source, Target, Effect, Mechanism. 2: Basic Quantitative Risk Models. 2.1. Risk as Probability of a Binary Event. 2.2. A Binary Event with Time: Hazard Rate Models. 2.3. Calculating and Interpreting Hazard Functions. 2.4. Hazard Models for Binary Events. 2.5. Probabilities of Causation for a Binary Event. 2.6. Risk Models with Non-Binary Consequences. 3: Health Risks from Human Activities. 3.1. Risk Management Decision Support Sub-Models. 2: Risk Assessment Modeling. 1: Introduction. 1.1. Approaches to QRA: Probability, Statistical, Engineering. 2: Conditional Probability Framework for Risk Calculations. 2.1. Calculating Average Individual Risks when Individuals Respond. 2.2. Population Risks Modeled by Conditional Probabilities. 2.3. Trees, Risks and Martingales. 2.4. Value of Information in Risk Management Decisions. 3: Basic Engineering Modeling Techniques. 3.1. Compartmental Flow Simulation Models. 3.2. Applications to Pharmacokinetic Models. 3.3. Monte Carlo Uncertainty Analysis. 3.4. Applied Probability and Stochastic Transition Models. 4: Introduction to Exposure Assessment. 5: A Case Study: Simulating Food Safety. 5.1. Background: The Potential Human Health Hazard. 5.2. Risk Management Setting: Many Decisions Affect Risk. 5.3. Methods and Data: Overviewof Simulation Model. 5.4. Results: Baseline and Sensitivity Analysis of Options. 5.5. Uncertainty Analysis and Discussion. 5.6: Conclusions. 3: Statistical Risk Modeling. 1: Introduction. 2: Statistical Dose-Response Modeling. 2.1. Define Exposure and Response Variables, Collect Data. 2.2. Select a Model Form for the Dose-Response Relation. 2.3. Estimate Risk, Confidence Limits, and Model Fit. 2.4. Interpret Results. 3: Progress in Statistical Risk Modeling. 3.1. Dealing with Model Uncertainty and Variable Selection. 3.2. Dealing with Missing Data: New Algorithms and Ideas. 3.3. Mixture Distribution Models for Unobserved Variables. 3.4. Summary of Advances in Statistical Risk Modeling. 4: A Statistical Case Study: Soil Sampling. 4: Causality. 1: Introduction. 2: Statistical vs. Causal Risk Modeling. 3: Criteria for Causation. 3.1. Traditional Epidemiological Criteria for Causation. 3.2. Proposed Criteria for Inferring Probable Causation. 3.3. Bayesian Evidential Reasoning and Refutationism. 4: Testing Causal Graph Models with Data. 4.1. Causal Graph Models and Knowledge Representation. 4.2. Meaning of Causal Graphs. 4.3. Testing Hypothesized Causal Graph Structures. 4.4. Creating Causal Graph Structures from Data. 4.5. Search, Optimization, and Model-Averaging Heuristics. 5: Using Causal Graphs in Risk Analysis. 5.1. Drawing Probabilistic Inferences in DAG Models. 5.2. Applications of DAG Inferences in Risk Assessment. 5.3.