Identification and review of sensitivity analysis methods.
Identification and qualitative comparison of sensitivity analysis methods that have been used across various disciplines, and that merit consideration for application to food-safety risk assessment models, are presented in this article. Sensitivity analysis can help in identifying critical control points, prioritizing additional data collection or research, and verifying and validating a model. Ten sensitivity analysis methods, including four mathematical methods, five statistical methods, and one graphical method, are identified. The selected methods are compared on the basis of their applicability to different types of models, computational issues such as initial data requirement and complexity of their application, representation of the sensitivity, and the specific uses of these methods. Applications of these methods are illustrated with examples from various fields. No one method is clearly best for food-safety risk models. In general, use of two or more methods, preferably with dissimilar theoretical foundations, may be needed to increase confidence in the ranking of key inputs.
Frey
H Christopher
HC
Department of Civil Engineering, North Carolina State University, Raleigh 27695-7908, USA. frey@eos.ncsu.edu
Patil
Sumeet R
SR
D000704:Analysis of Variance; D005506:Food Contamination / Q000517:prevention & control / Q000706:statistics & numerical data; D005583:Fourier Analysis; D015233:Models, Statistical; D012044:Regression Analysis; D018570:Risk Assessment; D012449:Safety; D012680:Sensitivity and Specificity; D014481:United States