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

Author

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  • A. E. Ades

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

  • S. Cliffe

    (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)

Abstract

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.

Suggested Citation

  • A. E. Ades & S. Cliffe, 2002. "Markov Chain Monte Carlo Estimation of a Multiparameter Decision Model: Consistency of Evidence and the Accurate Assessment of Uncertainty," Medical Decision Making, , vol. 22(4), pages 359-371, August.
    • Handle: RePEc:sae:medema:v:22:y:2002:i:4:p:359-371
    DOI: 10.1177/0272989X0202200414
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    Cited by:

    1. A. E. Ades & Karl Claxton & Mark Sculpher, 2006. "Evidence synthesis, parameter correlation and probabilistic sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 373-381, April.
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    3. N. J. Cooper & A. J. Sutton & A. E. Ades & S. Paisley & D. R. Jones & on behalf of the working group on the ‘use of evidence in economic decision models’, 2007. "Use of evidence in economic decision models: practical issues and methodological challenges," Health Economics, John Wiley & Sons, Ltd., vol. 16(12), pages 1277-1286, December.
    4. N. J. Cooper & A. J. Sutton & A. E. Ades & S. Paisley & D. R. Jones, 2007. "Use of evidence in economic decision models: practical issues and methodological challenges," Health Economics, John Wiley & Sons, Ltd., vol. 16(12), pages 1277-1286.
    5. Isabelle Albert & Emmanuelle Espié & Henriette de Valk & Jean‐Baptiste Denis, 2011. "A Bayesian Evidence Synthesis for Estimating Campylobacteriosis Prevalence," Risk Analysis, John Wiley & Sons, vol. 31(7), pages 1141-1155, July.
    6. Pamela M. McMahon & Alan M. Zaslavsky & Milton C. Weinstein & Karen M. Kuntz & Jane C. Weeks & G. Scott Gazelle, 2006. "Estimation of Mortality Rates for Disease Simulation Models Using Bayesian Evidence Synthesis," Medical Decision Making, , vol. 26(5), pages 497-511, September.
    7. A. Goubar & A. E. Ades & D. De Angelis & C. A. McGarrigle & C. H. Mercer & P. A. Tookey & K. Fenton & O. N. Gill, 2008. "Estimates of human immunodeficiency virus prevalence and proportion diagnosed based on Bayesian multiparameter synthesis of surveillance data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 541-580, June.
    8. A. E. Ades & G. Lu, 2003. "Correlations Between Parameters in Risk Models: Estimation and Propagation of Uncertainty by Markov Chain Monte Carlo," Risk Analysis, John Wiley & Sons, vol. 23(6), pages 1165-1172, December.
    9. Karl Claxton & Mark Sculpher & Chris McCabe & Andrew Briggs & Ron Akehurst & Martin Buxton & John Brazier & Tony O'Hagan, 2005. "Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 339-347, April.
    10. A. E. Ades & A. J. Sutton, 2006. "Multiparameter evidence synthesis in epidemiology and medical decision‐making: current approaches," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 5-35, January.

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