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Sensitivity Analysis of a Two‐Dimensional Probabilistic Risk Assessment Model Using Analysis of Variance
Author(s) -
Mokhtari Amirhossein,
Frey H. Christopher
Publication year - 2005
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.2005.00679.x
Subject(s) - sensitivity (control systems) , categorical variable , variance (accounting) , probabilistic logic , statistics , uncertainty analysis , computer science , variance based sensitivity analysis , probabilistic risk assessment , econometrics , risk analysis (engineering) , data mining , mathematics , analysis of variance , engineering , one way analysis of variance , medicine , accounting , electronic engineering , business
This article demonstrates application of sensitivity analysis to risk assessment models with two‐dimensional probabilistic frameworks that distinguish between variability and uncertainty. A microbial food safety process risk (MFSPR) model is used as a test bed. The process of identifying key controllable inputs and key sources of uncertainty using sensitivity analysis is challenged by typical characteristics of MFSPR models such as nonlinearity, thresholds, interactions, and categorical inputs. Among many available sensitivity analysis methods, analysis of variance (ANOVA) is evaluated in comparison to commonly used methods based on correlation coefficients. In a two‐dimensional risk model, the identification of key controllable inputs that can be prioritized with respect to risk management is confounded by uncertainty. However, as shown here, ANOVA provided robust insights regarding controllable inputs most likely to lead to effective risk reduction despite uncertainty. ANOVA appropriately selected the top six important inputs, while correlation‐based methods provided misleading insights. Bootstrap simulation is used to quantify uncertainty in ranks of inputs due to sampling error. For the selected sample size, differences in F values of 60% or more were associated with clear differences in rank order between inputs. Sensitivity analysis results identified inputs related to the storage of ground beef servings at home as the most important. Risk management recommendations are suggested in the form of a consumer advisory for better handling and storage practices.