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Methods to elicit beliefs for Bayesian priors: a systematic review
Sindhu R Johnson1, George A Tomlinson, Gillian A Hawker
1Division of Rheumatology, Department of Medicine, University Health Network, Toronto, Ontario M5T 2S8, Canada. Sindhu.Johnson@uhn.on.ca
Journal of Clinical Epidemiology
|September 1, 2009
Summary
Evaluating Bayesian belief elicitation methods is crucial. Few studies assess the validity, reliability, and responsiveness of these methods, highlighting a need for comparative research in Bayesian analysis.
Area of Science:
- Methodology in Bayesian statistics
- Measurement science in clinical research
Background:
- Bayesian analysis integrates clinician beliefs into treatment effect estimation.
- Numerous belief elicitation methods exist, but their comparative advantages are unclear.
- Measurement science principles (validity, reliability, responsiveness) are key to evaluating these methods.
Purpose of the Study:
- To review belief elicitation methods for Bayesian analysis.
- To determine if any methods offer incremental value based on measurement science principles.
- To assess the evaluation of validity, reliability, and responsiveness in existing methods.
Main Methods:
- Systematic review of multiple databases (MEDLINE, EMBASE, etc.).
- Search terms included variations of 'prior', 'beliefs', 'elicitation', and 'Bayesian'.
- Studies were evaluated on design, question stem, response options, analysis, and consideration of validity, reliability, and responsiveness.
Main Results:
- 33 studies on Bayesian belief elicitation methods were identified.
- Most elicitation occurred in cross-sectional studies (89%) for point estimates (58%).
- While many studies considered validity (64%), reliability (24%), and responsiveness (12%), few formally tested them (validity: 12%, reliability: 6%, responsiveness: 0%).
Conclusions:
- Methods for eliciting Bayesian prior beliefs have been summarized.
- The evaluation of elicitation methods' validity, reliability, and responsiveness is infrequent.
- Comparative studies are needed; until then, strategies to mitigate elicitation bias are recommended.
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