Measuring uncertainty in complex decision analysis models

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 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.