An internet-based method to elicit experts' beliefs for Bayesian priors: a case study in intracranial stent evaluation.
BACKGROUND
Bayesian methods provide an interesting approach to assessing an implantable medical device (IMD) that has evolved through successive versions because they allow for explicit incorporation of prior knowledge into the analysis. However, the literature is sparse on the feasibility and reliability of elicitation in cases where expert beliefs are used to form priors.


OBJECTIVE
To develop an Internet-based method for eliciting experts' beliefs about the success rate of an intracranial stenting procedure and to assess their impact on the estimated benefit of the latest version.


METHODS
The elicitation questionnaire was administered to a group of nineteen experts. Elicited experts' beliefs were used to inform the prior distributions of a Bayesian hierarchical meta-analysis model, allowing for the estimation of the success rate of each version.


RESULTS
Experts believed that the success rate of the latest version was slightly higher than that of the previous one (median: 80.8 percent versus 75.9 percent). When using noninformative priors in the model, the latest version was found to have a lower success rate (median: 83.1 percent versus 86.0 percent), while no difference between the two versions was detected with informative priors (median: 85.3 percent versus 85.6 percent).


CONCLUSIONS
We proposed a practical method to elicit experts' beliefs on the success rates of successive IMD versions and to explicitly combine all available evidence in the evaluation of the latest one. Our results suggest that the experts were overoptimistic about this last version. Nevertheless, the proposed method should be simplified and assessed in larger, representative samples.
Univ Paris Diderot;Sorbonne Paris Cité;INSERM UMR 717;Hop Saint-Louis,Service de Biostatistique et information Médicalel.pibouleau@has-sante.fr.
Leslie
L
Pibouleau
Univ Paris Diderot;Sorbonne Paris Cité;INSERM UMR 717;Hop Saint-Louis,Service de Biostatistique et information Médicale.
Sylvie
S
Chevret
D001499:Bayes Theorem; D016022:Case-Control Studies; D006801:Humans; D020407:Internet; D002532:Intracranial Aneurysm / Q000601:surgery*; D015607:Stents / Q000592:standards*; D011795:Surveys and Questionnaires; D013673:Technology Assessment, Biomedical / Q000379:methods*
Medical device