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

Published online by Cambridge University Press:  16 February 2015

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]

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.

Type
Methods
Copyright
Copyright © Cambridge University Press 2015 

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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. 1999:271275.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;24:110121.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. 1999:19:187203.Google Scholar
7. Leal, J, Wordsworth, S, Legood, R, Blair, E. Eliciting expert opinion for economic models: An applied example. Value Health. 2007;10:195203.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;24:350357.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;63:370383.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, 190.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;463:294295.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;6:14181424.Google Scholar
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