Bayesian Communication
A Clinically Significant Paradigm for Electronic Publication
- Correspondence and reprints: Harold P. Lehmann, MD, PhD, Johns Hopkins, Blalock 407, 600 N. Wolfe Street, Baltimore, MD 21287-4461; e-mail: 〈Lehmann{at}jhmi.edu〉
- Received 18 August 1999
- Accepted 17 January 2000
Abstract
Objective To develop a model for Bayesian communication to enable readers to make reported data more relevant by including their prior knowledge and values.
Background To change their practice, clinicians need good evidence, yet they also need to make new technology applicable to their local knowledge and circumstances. Availability of the Web has the potential for greatly affecting the scientific communication process between research and clinician. Going beyond format changes and hyperlinking, Bayesian communication enables readers to make reported data more relevant by including their prior knowledge and values. This paper addresses the needs and implications for Bayesian communication.
Formulation Literature review and development of specifications from readers', authors', publishers', and computers' perspectives consistent with formal requirements for Bayesian reasoning.
Results Seventeen specifications were developed, which included eight for readers (express prior knowledge, view effect size and variability, express threshold, make inferences, view explanation, evaluate study and statistical quality, synthesize multiple studies, and view prior beliefs of the community), three for authors (protect the author's investment, publish enough information, make authoring easy), three for publishers (limit liability, scale up, and establish a business model), and two for computers (incorporate into reading process, use familiar interface metaphors). A sample client-only prototype is available at http://omie.med.jhmi.edu/bayes.
Conclusion Bayesian communication has formal justification consistent with the needs of readers and can best be implemented in an online environment. Much research must be done to establish whether the formalism and the reality of readers' needs can meet.
Footnotes
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This work was supported by grant R29-LM05647-02 from the National Library of Medicine.
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↵* References 6 7 9 10 12 13 18 19 21 25 26 28 29 30 31 32 33 34 35 36 37.
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↵† Named after Pierre-Simon Laplace (1749-1827), the 19th century proponent of Bayesian reasoning. The first project in this series, THOMAS, was named after the Reverend Bayes (1701-1761) himself.
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↵‡ These numbers are used in the example of the prototype; see Validation by Example, below.
