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An Introduction to a Bayesian Method for Meta-analysis

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  • David M. Eddy
  • Vic Hasselblad
  • Ross Shachter

Abstract

The Confidence Profile Method is a new Bayesian method that can be used to assess technologies where the available evidence involves a variety of experimental designs, types of outcomes, and effect measures; a variety of biases; combinations of biases and nested biases; uncertainty about biases; an underlying variability in the parameter of interest; indirect evidence; and technology families. The result of an analysis with the Confidence Profile Method is a posterior distribution for the parameter of interest, posterior distributions for other parameters, and a covariance matrix for all the parameters in the model. The posterior distributions incorporate all the uncertainty the assessor chooses to describe about any of the parameters used in the analysis. Key words : Confidence Profile Method; bias; Bayesian analysis; meta-analysis. (Med Decis Making 1990;10:15-23)

Suggested Citation

  • David M. Eddy & Vic Hasselblad & Ross Shachter, 1990. "An Introduction to a Bayesian Method for Meta-analysis," Medical Decision Making, , vol. 10(1), pages 15-23, February.
    • Handle: RePEc:sae:medema:v:10:y:1990:i:1:p:15-23
    DOI: 10.1177/0272989X9001000104
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    References listed on IDEAS

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    1. David M. Eddy, 1989. "The Confidence Profile Method: A Bayesian Method for Assessing Health Technologies," Operations Research, INFORMS, vol. 37(2), pages 210-228, April.
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    1. Raymond C. W. Hutubessy & Louis W. Niessen & Rob F. Dijkstra & Ton F. Casparie & Frans F. Rutten, 2005. "Stochastic league tables: an application to diabetes interventions in the Netherlands," Health Economics, John Wiley & Sons, Ltd., vol. 14(5), pages 445-455, May.
    2. Martin Hellmich & Keith R. Abrams & Alex J. Sutton, 1999. "Bayesian Approaches to Meta-analysi of ROC Curves," Medical Decision Making, , vol. 19(3), pages 252-264, August.
    3. 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.

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