Foundations, Models and Methods
Nachdruck d. Ausg. v. 2001,
Band: 45, 556 Seiten, Gebunden, HC runder Rücken kaschiert, Format (B × H): 160 mm x 235 mm, Gewicht: 1027 g
Reihe: International Series in Operations Research & Management Science
Verlag: Springer, Berlin
Cox Jr. Risk Analysis Foundations, Models, and MethodsRisk Analysis: Foundations, Models, and Methods fully addresses the questions of "What is health risk analysis?" and "How can its potentialities be developed to be most valuable to public health decision-makers and other health risk managers?" Risk analysis provides methods and principles for answering these questions. It is divided into methods for assessing, communicating, and managing health risks. Risk assessment quantitatively estimates the health risks to individuals and to groups from hazardous exposures and from the decisions or activities that create them. It applies specialized models and methods to quantify likely exposures and their resulting health risks. Its goal is to produce information to improve decisions. It does this by relating alternative decisions to their probable consequences and by identifying those decisions that make preferred outcomes more likely. Health risk assessment draws on explicit engineering, biomathematical, and statistical consequence models to describe or simulate the causal relations between actions and their probable effects on health. Risk communication characterizes and presents information about health risks and uncertainties to decision-makers and stakeholders. Risk management applies principles for choosing among alternative decision alternatives or actions that affect exposure, health risks, or their consequences.
Weitere Infos & Material
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.