Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-22T23:48:06.075Z Has data issue: false hasContentIssue false
  • English
  • Français

USEOF EXPERT KNOWLEDGE ELICITATION TO ESTIMATE PARAMETERS IN HEALTH ECONOMIC DECISION MODELS

Part of: Special Collection - Methods

Published online by Cambridge University Press:  16 February 2015

David Hadorn ,
Giorgi Kvizhinadze ,
Lucie Collinson  and
Tony Blakely
David Hadorn
Affiliation:
Burden of Disease Epidemiology, Equity and Cost Effectiveness Programme, Department of Public Health, University of Otago, Wellington, PO Box 7343, Wellington, New [email protected]
Giorgi Kvizhinadze
Affiliation:
Burden of Disease Epidemiology, Equity and Cost Effectiveness Programme, Department of Public Health, University of Otago, Wellington, PO Box 7343, Wellington, New [email protected]
Lucie Collinson
Affiliation:
Burden of Disease Epidemiology, Equity and Cost Effectiveness Programme, Department of Public Health, University of Otago, Wellington, PO Box 7343, Wellington, New [email protected]
Tony Blakely
Affiliation:
Burden of Disease Epidemiology, Equity and Cost Effectiveness Programme, Department of Public Health, University of Otago, Wellington, PO Box 7343, Wellington, New [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: The aim of this study was to determine the prevalence and methods of expert knowledge elicitation (EKE) for specifying input parameters in health economic decision models (HEDM).

Methods: We created two samples using the National Health System Economic Evaluations Database: (1) 100 randomly selected HEDM studies to determine prevalence of EKE and (2) sixty studies using a formal EKE process to determine methods used.

Results: Fifty-seven (57 percent) of the random sample included at least one EKE-derived parameter. Of these, six (10 percent) used a formal expert process. Thirty-four studies from our second sample of sixty studies (57 percent) described at least one aspect of the process (e.g., elicitation method) with reasonable clarity. In approximately two-thirds of studies the external experts estimated parameters de novo; the remainder confirmed or modified initial estimates provided by authors, or the method was unclear. The majority of elicitations obtained point estimates only, although a few studies asked experts to estimate ranges of parameter values.

Conclusions: The use of EKE for parameter estimation is common in HEDMs, although there is room for improvement in the methods used.

Keywords

Expert opinionHealth knowledgeAttitudesPracticeDecision modelingAssessmentBiomedical technology
Type
Methods
Information
International Journal of Technology Assessment in Health Care , Volume 30 , Issue 4 , October 2014 , pp. 461 - 468
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES 1

1. van der Gaag, LC, Renooij, S, Witteman, CL, Aleman, BM, Taal, BG. Probabilities for a probabilistic network: A case study in oesophageal cancer. Artif Intell Med. 2002;25:123–48.Google Scholar
2. Christiansen, F, Nilsson, T, Mare, K, Carlsson, A. Adding a visual linear scale probability to the PIOPED probability of pulmonary embolism. Acta Radiol. 1997;38:458–63.Google Scholar
3. Harmanec, D, Leong, TY, Sundaresh, S, et al. Decision analytic approach to severe head injury management. Proc AMIA Symp. 1999:271–275.Google Scholar
4. Tan, SB, Chung, YF, Tai, BC, Cheung, YB, Machin, D. Elicitation of prior distributions for a phase III randomized controlled trial of adjuvant therapy with surgery for hepatocellular carcinoma. Control Clin Trials. 2003;24:110–121.Google Scholar
5. O’Hagan, A, Buck, CE, Daneshkhah, A, et al. Uncertain judgements: Eliciting experts’ probabilities. Chichester, England: John Wiley & Sons Ltd; 2006.CrossRefGoogle Scholar
6. Clemen, RT, Winkler, RL. Combining probability distributions from experts in risk analysis. Risk Anal. 1999:19:187–203.Google Scholar
7. Leal, J, Wordsworth, S, Legood, R, Blair, E. Eliciting expert opinion for economic models: An applied example. Value Health. 2007;10:195–203.CrossRefGoogle ScholarPubMed
8. Garthwaite, PH, Chilcott, JB, Jenkinson, DJ, Tappenden, P. Use of expert knowledge in evaluating costs and benefits of alternative service provisions: A case study. Int J Technol Assess Health Care. 2008;24:350–357.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. O’Hagan, A. SHELF: The Sheffield Elicitation Framework version 2.0. Sheffield, UK: University of Sheffield; 2010.Google Scholar
11. McKenna, C, McDaid, C, Suekarran, S, et al. Enhanced external counterpulsation for the treatment of stable angina and heart failure: A systematic review and economic analysis. Health Technol Assess. 2009;13:iii-iv, ix-xi, 1–90.Google Scholar
12. Cooke, RM. Experts in uncertainty: Opinion and subjective probability in science. Oxford: Oxford University Press; 1991.Google Scholar
13. Aspinall, W. A route to more tractable expert advice. Nature. 2010;463:294–295.Google Scholar
14. Tyshenko, MG, Darshan, S. Summary report of the expert elicitation workshop results for iatrogenic prion disease risks in Canada. Expert Elicitation Workshop Summary Report. Ottawa, Canada: University of Ottawa; 2009.Google Scholar
15. Briggs, A, Fenwick, E, Karnon, J, et al. DRAFT model parameter estimation and uncertainty: Report of the ISPOR-SMDM Modeling Good Research Practices Task Force - 6 Model Parameter Estimation and Uncertainty. Glasgow, Scotland, UK: Health Economics & Health Technology Assessment, Institute of Health & Wellbeing, University of Glasgow; 2010.Google Scholar
16. Andersson, KL, Salomon, JA, Goldie, SJ, Chung, RT. Cost effectiveness of alternative surveillance strategies for hepatocellular carcinoma in patients with cirrhosis. Clin Gastroenterol Hepatol. 2008;6:1418–1424.Google Scholar
Supplementary material: File

Hadorn et al. supplementary material

Supplementary figure

Download Hadorn et al. supplementary material(File)
File 47.3 KB
Supplementary material: File

Hadorn et al. supplementary material

Supplementary table 1

Download Hadorn et al. supplementary material(File)
File 34.5 KB
Supplementary material: File

Hadorn et al. supplementary material

Supplementary table 2

Download Hadorn et al. supplementary material(File)
File 30.8 KB
Supplementary material: File

Hadorn et al. supplementary material

Supplementary table 3

Download Hadorn et al. supplementary material(File)
File 22.5 KB