Royal Statistical Society
Statistical Computing Section Meetings
Sensitivity Analysis Meeting and AGM
The Section AGM will immediately precede this meeting
"Sensitivity analysis as a tool for model assessment"
Marian Scott (Department of Statistics, University of Glasgow
)
Synopsis: Sensitivity analysis (SA) is a general methodology used to evaluate the sensitivity of model output to changes in model input, i.e. the rate of change of the response function relative to the input parameters. There are a number of different methods for carrying out a sensitivity analysis, ranging from simple one-at-a-time methods to global, multivariate methods. There are also strong links to classical design of experiments. SA is closely linked to Uncertainty Analysis (UA), another computational method, where the objective is to evaluate the uncertainty on the model response as a result of uncertainties on the model input parameters (parametric uncertainty) and on the model form itself (structural uncertainty). In this talk, SA and UA tools, their use and the challenges presented in their application to some complex models will be discussed.
"Modelling, Making Inferences and Making Decisions: the role of sensitivity analysis"
Simon French (Manchester Business School
)
Synopsis: Sensitivity analysis, robustness studies and uncertainty
analyses are key stages in the modelling, inference and evaluation
used in operational research, decision analytic and risk management
studies. However, sensitivity methods - or others so similar technically
that they are difficult to distinguish from sensitivity methods -
are used in many different circumstances for many different purposes;
and the manner of their use in one context may be inappropriate in another.
In this talk, I categorise and explore the use of
sensitivity analysis and its parallels, and in doing so
hope to provide a guide and typology to a large growing
literature.
"Sensitivity Analysis using Value of Information"
Jeremy Oakley (University of Sheffield
)
Synopsis: When using a computer model to guide a decision, the decision-
maker will need to understand the uncertainty present in the model.
One source of uncertainty will typically be the values of various input
parameters in the model that should be used for the situation at
hand. Sensitivity analysis is then concerned with identifying which
of these parameters is influential in some sense in driving the model
output uncertainty. The value of information approach to sensitivity
analysis directly relates the influence of each uncertain input
parameter to the decision problem in question; it quantifies the value
to the decision maker of learning the true value of any uncertain
input parameter before making their final decision. Unfortunately,
severe computational problems can arise when calculating these
measures of sensitivity for complex models. In this talk the value of
information framework for sensitivity analysis is reviewed and
efficient computational tools for complex models are presented. The
talk is motivated by an application in medical decision making using
health economic models.
