Get More From Your Models - Use Sensitivity Analysis
Full-Day Workshop
Sunday, December 6, 2009
Renaissance Inner Harbor
Baltimore, MD
Register via the Society for Risk Analysis.
Description
This workshop will answer key questions faced by those who conduct, manage, or review probabilistic and sensitivity analysis of risk models. When should you perform sensitivity analysis? What are the typical simulation techniques and software packages? What are the roles of uncertainty and sensitivity analyses as value added techniques in risk assessment? How do you prepare a model to facilitate sensitivity analysis? What are key considerations in the development of scenarios that are the basis for sensitivity analysis? What are some typical sensitivity analysis methods and how can you select among them? How should particular sensitivity analysis methods be applied? How should the results of sensitivity analysis be presented and interpreted? This workshop will answer these questions.
The methods and case studies presented are
based upon several years of research at NC State University (Dr. Frey) and RTI
International regarding developing quantitative risk assessment models for
environmental and microbial systems and research regarding transferring,
applying, and adapting sensitivity analysis methods developed in other
disciplines (e.g. complex engineering systems) to quantitative exposure and
risk assessment models. Results included a guidance document regarding
selection, application, and interpretation of sensitivity analysis methods
applied to quantitative risk assessment models. This workshop helps practitioners select specific sensitivity analysis methods
relevant to the particular case study and model characteristics. The workshop
will also aid in interpreting results from a sensitivity analysis in response
to a particular modeling objective. Participants will be provided with course
notes, a copy of the guidance document, and a tutorial with examples for how to
perform sensitivity analysis using common methods. The basic concepts of
probabilistic risk assessment will be illustrated using software packages such
as @Risk and Crystal Ball. The uncertainty and sensitivity analysis methods
will also be illustrated with practical case studies. This workshop is aimed at
practitioners, managers, or reviewers who wish to refine their knowledge
regarding approaches in risk assessment and sensitivity analysis methods.
Draft Agenda/Syllabus:
Introduction of Presenters (Morning)
Amir Mokhtari
Chris Frey
Introduction and background (Morning)
Common risk assessment paradigms
Differences between chemical and microbial risk assessment frameworks
Definitions of variability and uncertainty
Understanding deterministic and stochastic processes
Two-dimensional simulation models
Designing
and implementing a two-dimensional
Advanced techniques in quantitative risk assessment: the concept of sensitivity analysis
(Morning)
Sensitivity analysis vs. importance
Motivation of the use of sensitivity analysis in exposure or risk assessment
Situations in which it is necessary to perform sensitivity analysis
Preparation of existing models and new models
for sensitivity analysis
How are Existing and New Models Prepared to Facilitate Sensitivity Analysis? (Morning)
Preparation of Existing Models for Sensitivity Analysis
Identification
of Model Structure
Identification
of Inputs
Selection
of Model Outputs for Sensitivity Analysis
Probabilistic
Simulation and Implication for Sensitivity Analysis
Modifications of the Model
Preparation of New Models for Sensitivity Analysis
Modeling
Environment
Characterizing
Variability and Uncertainty in the Probabilistic Simulation
Modeling
Strategies
Model Documentation
Defining the case study scenarios for sensitivity analysis (Afternoon)
Conceptual framework for defining a case study scenario
Identification of susceptible sub-population
Identification of pathways of exposure
Identification of temporal and spatial dimensions of the case study
Probabilistic approaches
Scenario uncertainty
Available methods for sensitivity analysis and general application procedures (Afternoon)
Mathematical methods
Nominal
sensitivity analysis
Differential
sensitivity analysis
Statistical methods
Correlation
coefficients
Regression
analysis
Analysis
of variance (ANOVA)
Classification
and regression trees (CART)
Fourier
amplitude sensitivity test (FAST)
Sobol’s method
Graphical methods
Scatter
plots
Conditional sensitivity analysis
General procedures for applying the methods to exposure or risk assessment models
Deterministic
versus probabilistic
Identifying
nominal values for inputs
Need
for normalization
Add-hoc
versus integrated approach
Method demonstration: Step-by-step tutorial with examples
Selection of sensitivity analysis methods (Afternoon)
Key questions for selection of sensitivity analysis methods
What are the objectives of sensitivity analysis?
Based upon the objectives what information is needed from sensitivity analysis?
What are the characteristics of the model that constrain or indicate preference regarding method selection?
How detailed is the analysis?
What are the characteristics of the software that may constrain the selection of methods?
What are the specifications of computing resources?
Can “push-button” methods adequately address the characteristics of interest in the analysis?
Is the implementation of the selected sensitivity analysis method post hoc?
Decision framework to assist in selecting sensitivity analysis methods
Practical
case studies (Afternoon)
Discussion (Afternoon)
Wrap-up
Presenters:
|
Amir Mokhtari |
|
RTI International |
|
Dr. Mokhtari is currently a Research Environmental Scientist at RTI International and has extensive experience in environmental and microbial food safety exposure and risk assessment, quantification of variability and uncertainty, and sensitivity analysis of probabilistic and stochastic models. |
|
Chris Frey |
|
North Carolina |
|
Dr. Frey is a Professor of Civil Engineering at |