Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-24T10:32:35.014Z Has data issue: false hasContentIssue false
  • English
  • Français

A Bayesian Method for Synthesizing Evidence: The Confidence Profile Method

Published online by Cambridge University Press:  10 March 2009

David M. Eddy ,
Vic Hasselblad  and
Ross Shachter
David M. Eddy
Affiliation:
Duke University
Vic Hasselblad
Affiliation:
Duke University
Ross Shachter
Affiliation:
Duke University
Get access Rights & Permissions [Opens in a new window]

Extract

This article describes a collection of meta-analysis techniques based on Bayesian statistics for interpreting, adjusting, and combining evidence to estimate parameters and outcomes important to the assessment of health technologies. The result of an analysis by the Confidence Profile Method is a joint posterior probability distribution for the parameters of interest, from which marginal distributions for any particular parameter can be calculated. The method can be used to analyze problems involving a variety of types of outcomes, a variety of measures of effect, and a variety of experimental designs. This article presents the elements necessary for analysis, including prior distributions, likelihood functions, and specific models for experimental designs that include adjustment for biases.

Type
Special Section: Alternative Methods for Assessing Technology, Part II
Information
International Journal of Technology Assessment in Health Care , Volume 6 , Issue 1 , January 1990 , pp. 31 - 55
Copyright
Copyright © Cambridge University Press 1990

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Basu, D.On the elimination of nuisance parameters. Journal of the American Statistical Association, 1977, 72, 355–66.CrossRefGoogle Scholar
Berger, J. O.Statistical decision theory and Bayesian analysis. New York: Springer-Verlag, 1984.Google Scholar
Berger, J., & Wolpert, R.The likelihood principle, 2nd edition. Hayward, CA: Mathematical Statistics, 1988.CrossRefGoogle Scholar
Breslow, N. E., & Day, N. E.Statistical methods in cancer research. Vol. 1. The analysis of case-control studies. Lyon, France: IARC, 1980.Google Scholar
DeGroot, M. H.Optimal statistical decisions. New York: McGraw-Hill, 1970.Google Scholar
Eddy, D. M. The use of confidence profiles to assess tissue-type plasminogen activator. In Wagner, G. S. & Califf, R. (eds.), Acute coronary care 1987. New York: Martinus Nihjoff Publishing Co., 1986.Google Scholar
Eddy, D. M., Hasselblad, V., McGivney, W., & Hendee, W.The value of mammography screening in women under age 50. Journal of the American Medical Association, 1988, 259, 1512–19.CrossRefGoogle ScholarPubMed
Eddy, D. M.The Confidence Profile Method: A Bayesian method for assessing health technologies. Operations Research, 1989, 37, 210–28.CrossRefGoogle Scholar
Jeffreys, H.Theory of probability. London: Oxford University Press, 1961.Google Scholar
Laird, N. M., & Mosteller, F.Methods for interpreting and combining experimental results. International Journal of Technology Assessment in Health Care, 1990, 6, 5–30.CrossRefGoogle Scholar
Shachter, R. D., Eddy, D. M., & Hasselblad, V. An influence diagram approach to medical technology assessment. In Oliver, R. M. & Smith, J. Q. (eds.), Influence diagrams and belief nets. New York: Wiley, 1990.Google Scholar
Stead, A. G., Hasselblad, V., Creason, J. P., & Claxton, L.Modeling the Ames test. Mutation Research, 1981, 85, 13–27.CrossRefGoogle ScholarPubMed