Journal of Clinical Epidemiology
Volume 63, Issue 4 , Pages 355-369, April 2010

Methods to elicit beliefs for Bayesian priors: a systematic review

  • Sindhu R. Johnson

      Affiliations

    • Division of Rheumatology, Department of Medicine, University Health Network, Toronto, Ontario, Canada
    • Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
    • Corresponding Author InformationCorresponding author. Division of Rheumatology, University Health Network, Ground Floor, East Wing, Toronto Western Hospital, 399 Bathurst Street, Toronto, Ontario M5T 2S8, Canada. Tel.: +416-603-6417; fax +416-603-4348.
    • ,
  • George A. Tomlinson

      Affiliations

    • Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
    • Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
    • Division of Clinical Decision Making and Health Care, Toronto General Research Institute, Toronto, Ontario, Canada
    • ,
  • Gillian A. Hawker

      Affiliations

    • Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
    • Division of Rheumatology, Department of Medicine, Women's College Hospital, Toronto, Ontario, Canada
    • ,
  • John T. Granton

      Affiliations

    • Divisions of Respirology and Critical Care Medicine, Department of Medicine, University Health Network, Toronto, Ontario, Canada
    • ,
  • Brian M. Feldman

      Affiliations

    • Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
    • Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
    • Division of Rheumatology, Department of Paediatrics, The Hospital for Sick Children, Toronto, Ontario, Canada

Accepted 9 June 2009. published online 28 August 2009.

Abstract 

Objective

Bayesian analysis can incorporate clinicians' beliefs about treatment effectiveness into models that estimate treatment effects. Many elicitation methods are available, but it is unclear if any confer advantages based on principles of measurement science. We review belief-elicitation methods for Bayesian analysis and determine if any of them had an incremental value over the others based on its validity, reliability, and responsiveness.

Study Design and Setting

A systematic review was performed. MEDLINE, EMBASE, CINAHL, Health and Psychosocial Instruments, Current Index to Statistics, MathSciNet, and Zentralblatt Math were searched using the terms (prior OR prior probability distribution) AND (beliefs OR elicitation) AND (Bayes OR Bayesian). Studies were evaluated on: design, question stem, response options, analysis, consideration of validity, reliability, and responsiveness.

Results

We identified 33 studies describing methods for elicitation in a Bayesian context. Elicitation occurred in cross-sectional studies (n=30, 89%), to derive point estimates with individual-level variation (n=19; 58%). Although 64% (n=21) considered validity, 24% (n=8) reliability, 12% (n=4) responsiveness of the elicitation methods, only 12% (n=4) formally tested validity, 6% (n=2) tested reliability, and none tested responsiveness.

Conclusions

We have summarized methods of belief elicitation for Bayesian priors. The validity, reliability, and responsiveness of elicitation methods have been infrequently evaluated. Until comparative studies are performed, strategies to reduce the effects of bias on the elicitation should be used.

Keywords: Belief elicitation, Bayesian, Validity, Reliability, Bias, Priors

 

PII: S0895-4356(09)00175-9

doi:10.1016/j.jclinepi.2009.06.003

Journal of Clinical Epidemiology
Volume 63, Issue 4 , Pages 355-369, April 2010

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