A Bayesian approach to sensitivity analysis.
Sensitivity analysis has traditionally been applied to decision models to quantify the stability of a preferred alternative to parametric variation. In the health literature, sensitivity measures have traditionally been based upon distance metrics, payoff variations, and probability measures. We advocate a new approach based on information value and argue that such an approach is better suited to address the decision-maker's real concerns. We provide an example comparing conventional sensitivity analysis to one based on information value. This article is a US government work and is in the public domain in the United States.
Felli
J C
JC
Naval Postgraduate School, Monterey, CA 93943-5201, USA. jcfelli@nps.navy.mil
Hazen
G B
GB
D001499:Bayes Theorem; D003362:Cost-Benefit Analysis / Q000706:statistics & numerical data; D003661:Decision Support Techniques; D003663:Decision Trees; D006302:Health Services Research / Q000191:economics / Q000379:methods*; D006801:Humans; D017059:Models, Econometric; D010255:Paranasal Sinus Neoplasms / Q000628:therapy