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AN INTERNET-BASED METHOD TO ELICIT EXPERTS’ BELIEFS FOR BAYESIAN PRIORS: A CASE STUDY IN INTRACRANIAL STENT EVALUATION

Part of: Special Collection - Methods

Published online by Cambridge University Press:  17 November 2014

Leslie Pibouleau  and
Sylvie Chevret
Leslie Pibouleau
Affiliation:
Univ Paris Diderot; Sorbonne Paris Cité; INSERM UMR 717; Hop Saint-Louis, Service de Biostatistique et information Mé[email protected] Haute Autorité de Santé, Service Evaluation Economique et Santé Publique
Sylvie Chevret
Affiliation:
Univ Paris Diderot; Sorbonne Paris Cité; INSERM UMR 717; Hop Saint-Louis, Service de Biostatistique et information Médicale
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Abstract

Rationale: 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.

Objectives: 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.

Study Design and Setting: 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.

Keywords

Bayesian analysisPrior informationElicitation methodInternet-based surveyMedical device
Type
Methods
Information
International Journal of Technology Assessment in Health Care , Volume 30 , Issue 4 , October 2014 , pp. 446 - 453
Copyright
Copyright © Cambridge University Press 2014 

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References

REFERENCES

1. Campbell, G. Bayesian statistics in medical devices: Innovation sparked by the FDA. J Biopharm Stat. 2011;21:871–887.Google Scholar
2. Pibouleau, L, Chevret, S. Bayesian statistical method was underused despite its advantages in the assessment of implantable medical devices. J Clin Epidemiol. 2010;64:270–279.CrossRefGoogle ScholarPubMed
3. Pibouleau, L, Chevret, S. Bayesian hierarchical meta-analysis model for medical device evaluation: Application to intracranial stents. Int J Technol Assess Health Care. 2013;29:123–130.Google Scholar
4. Mangubat, EZ, Johnson, AK, Keigher, KM, Lopes, DK. Initial experience with neuroform EZ in the treatment of wide-neck cerebral aneurysms. Neurointervention. 2012;7:34–39.Google Scholar
5. O’Hagan, A. The Bayesian approach to statistics. http://www.sagepub.com/upm-data/18550_Chapter6.pdf (accessed October 20, 2013).Google Scholar
6. O’Hagan, A, Buck, CE, Daneshkhah, A, et al. Uncertain judgements - eliciting experts’ probabilities. New York: Wiley; 2006.Google Scholar
7. Johnson, SR, Tomlinson, GA, Hawker, GA, Granton, JT, Feldman, BM. Methods to elicit beliefs for Bayesian priors: A systematic review. J Clin Epidemiol. 2010;63:355–369.Google Scholar
8. Low-Choy, S. Expert elicitation and its interface with technology: A review with a view to designing Elicitator. unpublished. 2009. http://mssanz.org.au/modsim09/J2/lowchoy.pdf (accessed October 20, 2013).Google Scholar
9. Johnson, SR, Tomlinson, GA, Hawker, GA, et al. A valid and reliable belief elicitation method for Bayesian priors. J Clin Epidemiol. 2010;63:370–383.Google Scholar
10. Devilee, JLA, Knol, AB. Software to support expert elicitation. RIVM Lett Rep. 2011.Google Scholar
11. O’Hagan, A. Eliciting expert beliefs in substantial practical applications. Statistician. 1998;47 (Pt 1):21–35.Google Scholar
12. O’Hagan, A, Buck, CE, Daneshkhah, A, et al. Uncertain judgements - eliciting experts’ probabilities. New York: Wiley; 2006.Google Scholar
13. Winkler, RL. The quantification of judgment: Some methodological suggestions. J Am Stat Assoc. 1967;62:1105–1120.Google Scholar
14. Denham, R, Mengersen, K. Geographically assisted elicitation of expert opinion for regression models. Bayesian Anal. 2007;2:99–136.CrossRefGoogle Scholar
15. Garthwaite, PH, Al-Awadhi, SA, Elfadaly, FG, Jenkinson, DJ. Prior distribution elicitation for generalized linear and picewise-linear models. J Appl Stat. 2013;40:59–75.Google Scholar
16. Leal, J, Wordsworth, S, Legood, R, Blair, E. Eliciting expert opinion for economic models: An applied example. Value Health. 2007;10:195–203.Google Scholar
17. Abbas, AE, Budescu, DV, Yu, HT, Haggerty, R. A comparison of two probability encoding methods: Fixed probability vs. fixed variable values. Decision Anal. 2008;5:190–202.Google Scholar
18. Hora, SC. Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management. Reliab Eng Syst Saf. 1996;54:217–223.Google Scholar
19. Stone, M. The opinion pool. Ann Math Stat. 1961;32:1339–1342.Google Scholar
20. Phillips, LD, Wisbey, SJ. The elicitation of judgemental probability distributions from groupes of experts: A description of the methodology and records of seven formal elicitation sessions held in 1991 and 1992. Report NSS/B101. Didcot, UK: Nirex; 1993.Google Scholar
21. Soares, MO, Bojke, L, Dumville, J, et al. Methods to elicit experts’ beliefs over uncertain quantities: Application to a cost effectiveness transition model of negative pressure wound therapy for severe pressure ulceration. Stat Med. 2011;30:2363–2380.Google Scholar
22. Oakley, JE, O’Hagan, A. SHELF: The Sheffield Elicitation Framework (version 2.0). Sheffield, UK: School of Mathematics and Statistics, 2010. http://www.tonyohagan.co.uk/shelf/ (accessed October 20, 2013).Google Scholar
23. Sperber, D, Mortimer, D, Lorgelly, P, Berlowitz, D. An expert on every street corner? Methods for eliciting distributions in geographically dispersed opinion pools. Value Health. 2013;16:434–437.CrossRefGoogle ScholarPubMed
24. Clemen, RT, Winkler, RL. Combining probability distributions from experts in risk analysis. Risk Anal. 1999;19:187–203.Google Scholar
25. Philips, Z, Ginnelly, L, Sculpher, M, et al. Review of guidelines for good practice in decision-analytic modelling in health technology assessment. Health Technol Assess. 2004;8:iii–iv, ix–xi, 1–158.CrossRefGoogle ScholarPubMed
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