Measuring uncertainty in complex decision analysis models

Giovanni Parmigiani

Research output: Contribution to journalArticle

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 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.

Original languageEnglish (US)
Pages (from-to)513-537
Number of pages25
JournalStatistical Methods in Medical Research
Volume11
Issue number6
DOIs
StatePublished - Dec 2002

ASJC Scopus subject areas

  • Epidemiology
  • Health Information Management
  • Nursing(all)

Fingerprint Dive into the research topics of 'Measuring uncertainty in complex decision analysis models'. Together they form a unique fingerprint.

  • Cite this