title: Measuring uncertainty in complex decision analysis models.
abstract: Prediction models used in support of clinical and health policy decision making often need to consider the course of a disease over an extended period of time, and draw evidence from a broad knowledge base, including epidemiologic cohort and case control studies, randomized clinical trials, expert opinions, and more. This paper is a brief introduction to these complex decision models, their relation to Bayesian decision theory, and the tools typically used to describe the uncertainties involved. Concepts are illustrated throughout via a simplified tutorial.
authors.affiliation: The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, USA. gp@rhu.edu
authors.forename: G
authors.initials: G
authors.lastname: Parmigiani
mesh_terms: D001499:Bayes Theorem; D003362:Cost-Benefit Analysis; D003627:Data Interpretation, Statistical; D003661:Decision Support Techniques; D019317:Evidence-Based Medicine; D006302:Health Services Research / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D011336:Probability; D012680:Sensitivity and Specificity; D020521:Stroke / Q000517:prevention & control