This Article Free Full Text (Free PDF) Free References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Ades, A. E. Articles by Cliffe, S. Search for Related Content PubMed PubMed Citation Articles by Ades, A. E. Articles by Cliffe, S. Pubmed/NCBI databases Medline Plus Health Information High Risk Pregnancy Infections and Pregnancy Social Bookmarking                         What's this?

Markov Chain Monte Carlo Estimation of a Multiparameter Decision Model: Consistency of Evidence and the Accurate Assessment of Uncertainty

Medical Research Council, Health Services Research Collaboration, University of Bristol, Bristol, United Kingdom

S. Cliffe, MPH

Communicable Disease Surveillance Centre, Public Health Laboratory Service, Colindale, United Kingdom, and the Department of Epidemiology and Biostatistics, Institute of Child Health, University College London, London, United Kingdom

Decision models are usually populated 1 parameter at a time, with 1 item of information informing each parameter. Often, however, data may not be available on the parameters themselves but on several functions of parameters, and there may be more items of information than there are parameters to be estimated. The authors show how in these circumstances all the model parameters can be estimated simultaneously using Bayesian Markov chain Monte Carlo methods. Consistency of the information and/or the adequacy of the model can also be assessed within this framework. Statistical evidence synthesis using all available data should result in more precise estimates of parameters and functions of parameters, and is compatible with the emphasis currently placed on systematic use of evidence. To illustrate this, WinBUGS software is used to estimate a simple 9-parameter model of the epidemiology of HIV in women attending prenatal clinics, using information on 12 functions of parameters, and to thereby compute the expected net benefit of 2 alternative prenatal testing strategies, universal testing and targeted testing of high-risk groups. The authors demonstrate improved precision of estimates, and lower estimates of the expected value of perfect information, resulting from the use of all available data.

Key Words: evidence synthesis • decision analysis • Markov chain Monte Carlo • Bayesian methods • incremental net benefit • expected value of perfect information • epidemiology • HIV • screening

Medical Decision Making, Vol. 22, No. 4, 359-371 (2002)
DOI: 10.1177/0272989X0202200414


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?

This article has been cited by other articles:

				M. J. Sweeting, D. De Angelis, M. Hickman, and A. E. Ades Estimating hepatitis C prevalence in England and Wales by synthesizing evidence from multiple data sources. Assessing data conflict and model fit Biostat., October 1, 2008; 9(4): 715 - 734. [Abstract] [Full Text] [PDF]

				A E Ades, N. J Welton, D. Caldwell, M. Price, A. Goubar, and G. Lu Multiparameter evidence synthesis in epidemiology and medical decision-making J Health Serv Res Policy, October 1, 2008; 13(suppl_3): 12 - 22. [Abstract] [Full Text] [PDF]

				A. H Briggs New methods of analysing cost effectiveness BMJ, September 29, 2007; 335(7621): 622 - 623. [Full Text] [PDF]

				B. Groot Koerkamp, M.G. M. Hunink, T. Stijnen, J. K. Hammitt, K. M. Kuntz, and M. C. Weinstein Limitations of Acceptability Curves for Presenting Uncertainty in Cost-Effectiveness Analysis Med Decis Making, March 1, 2007; 27(2): 101 - 111. [Abstract] [PDF]

				P. M. McMahon, A. M. Zaslavsky, M. C. Weinstein, K. M. Kuntz, J. C. Weeks, and G. S. Gazelle Estimation of Mortality Rates for Disease Simulation Models Using Bayesian Evidence Synthesis Med Decis Making, September 1, 2006; 26(5): 497 - 511. [Abstract] [PDF]

				G. B. Hazen and M. Huang Large-Sample Bayesian Posterior Distributions for Probabilistic Sensitivity Analysis Med Decis Making, September 1, 2006; 26(5): 512 - 534. [Abstract] [PDF]

				N. J. Welton and A. E. Ades Estimation of Markov Chain Transition Probabilities and Rates from Fully and Partially Observed Data: Uncertainty Propagation, Evidence Synthesis, and Model Calibration Med Decis Making, November 1, 2005; 25(6): 633 - 645. [Abstract] [PDF]

				A. E. Ades, G. Lu, and J. P. T. Higgins The Interpretation of Random-Effects Meta-Analysis in Decision Models Med Decis Making, November 1, 2005; 25(6): 646 - 654. [Abstract] [PDF]

				N. Hawkins, D. Epstein, M. Drummond, J. Wilby, A. Kainth, D. Chadwick, and M. Sculpher Assessing the Cost-Effectiveness of New Pharmaceuticals in Epilepsy in Adults: The Results of a Probabilistic Decision Model Med Decis Making, September 1, 2005; 25(5): 493 - 510. [Abstract] [PDF]

				F. Yokota and K. M. Thompson Value of Information Literature Analysis: A Review of Applications in Health Risk Management Med Decis Making, June 1, 2004; 24(3): 287 - 298. [Abstract] [PDF]

				A. E. Ades, G. Lu, and K. Claxton Expected Value of Sample Information Calculations in Medical Decision Modeling Med Decis Making, March 1, 2004; 24(2): 207 - 227. [Abstract] [PDF]

				A. H. Briggs, A. E. Ades, and M. J. Price Probabilistic Sensitivity Analysis for Decision Trees with Multiple Branches: Use of the Dirichlet Distribution in a Bayesian Framework Med Decis Making, July 1, 2003; 23(4): 341 - 350. [Abstract] [PDF]