3. Introduction to Decision Models

  1. Nicky J. Welton1,
  2. Alexander J. Sutton2,
  3. Nicola J. Cooper2,
  4. Keith R. Abrams2 and
  5. A.E. Ades1

Published Online: 3 APR 2012

DOI: 10.1002/9781119942986.ch3

Evidence Synthesis for Decision Making in Healthcare

Evidence Synthesis for Decision Making in Healthcare

How to Cite

Welton, N. J., Sutton, A. J., Cooper, N. J., Abrams, K. R. and Ades, A.E. (2012) Introduction to Decision Models, in Evidence Synthesis for Decision Making in Healthcare, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781119942986.ch3

Author Information

  1. 1

    School of Social and Community Medicine, University of Bristol, UK

  2. 2

    Department of Health Sciences, University of Leicester, UK

Publication History

  1. Published Online: 3 APR 2012
  2. Published Print: 11 MAY 2012

ISBN Information

Print ISBN: 9780470061091

Online ISBN: 9781119942986

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Keywords:

  • decision models;
  • model parameters;
  • probability;
  • sources of evidence;
  • stochastic decision tree;
  • WinBUGS

Summary

This chapter focuses on the decision analytical models including: (i) consideration of the types of data required to populate such models; and (ii) the different approaches that can be taken to specify and evaluate such models. It discusses the model parameters under the four broad headings of effects of interventions, quantities relating to the clinical epidemiology of the underlying medical condition, utilities, and costs. A stochastic decision model evaluated within a Bayesian framework allows the decision maker to estimate useful probability statements such as the probability that a new treatment has greater expected payoffs compared with the existing treatment. The chapter outlines the premise for the use of decision models in cost-effectiveness analysis. Both deterministic and stochastic models have been considered and examples of their implementation in WinBUGS provided, where one piece of (independent) evidence per parameter was assumed to exist.

Controlled Vocabulary Terms

Bayesian estimation

Controlled Vocabulary Terms

Bayesian estimation; Bayesian inference using Gibbs sampling; Markov chain Monte Carlo; probability