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Measuring uncertainty in complex decision analysis models

The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, USA, gp{at}rhu.edu

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.

Statistical Methods in Medical Research, Vol. 11, No. 6, 513-537 (2002)
DOI: 10.1191/0962280202sm307ra


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