Bayesian communication: a clinically significant paradigm for electronic publication

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.

Figure 1A

Example of Bayes applet. Slider panel. User has specified prior belief (upper bar) in the difference between adenosine and normal saline as normally distributed, with a mean of 7 mm Hg and a 95 percent Bayesian confidence interval from -11.11 to +25.11. The machine describes, or explains, this prior belief as “Mildly in favor of adenosine.” The data from the study provide an estimate of 17, with a sampling confidence interval of +2.67 to +31.33 (middle bar), also explained by the machine as “Mildly in favor of adenosine.” The machine then presents the calculated posterior 95 percent Bayesian confidence interval (lower bar), +7.42 to +18.88, which is still “Mildly in favor.”

Figure 1B

Example of Bayes applet. Tail probability. The applet has calculated the probability of 0.989 that the true difference is greater than zero (i.e., that adenosine is better than normal saline placebo).

Figure 1C

Example of Bayes applet. Minimal clinically important difference and sensitivity analysis. The user has specified (see below) that 10 mm Hg is the threshold below which she would not change her clinical behavior. The graph shows a number of things. The confidence intervals taken from the slider panel (A) are represented as single points in this plane, where the x-axis represents the mean difference between the arms, and the y-axis represents certainty (inversely related to standard deviation). The applet has also calculated two regions—the area where prior beliefs would lead to a posterior belief less than 10 (light grey), and the area where prior beliefs would lead to a posterior belief greater than 10. The current prior is in the latter region.

Figure 1D

Example of Bayes applet. The probability that the difference is greater than the desired threshold is less than 95 percent. Data from Konduri et al.