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Use of expert knowledge in evaluating costs and benefits of alternative service provisions: A case study

Published online by Cambridge University Press:  04 July 2008

Paul H. Garthwaite ,
James B. Chilcott ,
David J. Jenkinson  and
Paul Tappenden
Paul H. Garthwaite
Affiliation:
The Open University
James B. Chilcott
Affiliation:
The University of Sheffield
David J. Jenkinson
Affiliation:
University of Aberdeen
Paul Tappenden
Affiliation:
The University of Sheffield
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Abstract

Objectives: A treatment pathway model was developed to examine the costs and benefits of the current bowel cancer service in England and to evaluate potential alternatives in service provision. To use the pathway model, various parameters and probability distributions had to be specified. They could not all be determined from empirical evidence and, instead, expert opinion was elicited in the form of statistical quantities that gave the required information. The purpose of this study is to describe the procedures used to quantify expert opinion and note examples of good practice contained in the case study.

Methods: The required information was identified and preparatory discussion with four experts refined the questions they would be asked. In individual elicitation sessions they quantified their opinions, mainly in the form of point and interval estimates for specified variables. New methods have been developed for quantifying expert opinion and these were implemented in specialized software that uses interactive graphics. This software was used to elicit opinion about quantities related to measurable covariates.

Results: Assessments for thirty-four quantities were elicited and available checks supported their validity. Eight points of good practice in eliciting and using expert judgment were evident. Parameters and probability distributions needed for the pathway model were determined from the elicited assessments. Simulation results from the pathway model were used to inform policy on bowel cancer service provision.

Conclusions: The study illustrates that quantifying and using expert judgment can be acceptable in real problems of practical importance. For full benefit to be gained from expert knowledge, elicitation must be conducted carefully and should be reported in detail.

Keywords

ElicitationProbability assessmentExpert opinionSubjective probabilityColorectal cancer
Type
RESEARCH REPORTS
Information
International Journal of Technology Assessment in Health Care , Volume 24 , Issue 3 , July 2008 , pp. 350 - 357
Copyright
Copyright © Cambridge University Press 2008

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References

REFERENCES

1. Berbaum, KS, Dorfman, DD, Franken, EA Jr, Caldwell, RT. An empirical comparison of discrete ratings and subjective probability ratings. Acad Radiol. 2002;9:756–763.CrossRefGoogle ScholarPubMed
2. Christiansen, F, Nilsson, T, Mare, K, Carlson, A. Adding a visual linear scale probability to the PIOPED probability of pulmonary embolism. Acta Radiol. 1997;38:458–463.CrossRefGoogle Scholar
3. Department of Health. The NHS cancer plan: A plan for investment, a plan for change. London: UK National Health Service; 2000.Google Scholar
4. Garthwaite, PH, Al-Awadhi, SA. Quantifying opinion about a logistic regression using interactive graphics. Technical report 06/07. Milton Keynes, UK: Department of Statistics, Open University; 2006.Google Scholar
5. Harmanec, D, Leong, TY, Sundaresh, S, et al. Decision analytic approach to severe head injury management. Proceedings of the 1999 AMIA Annual Symposium. Washington, DC. 1999:271–275.Google Scholar
6. O'Hagan, A, Buck, CE, Daneshkhah, A, Eiser, JR, Garthwaite, PH, Jenkinson, DJ, Oakley, JE, Rakow, T. Uncertain judgements: Eliciting experts’ probabilities. Chichester: John Wiley; 2006.CrossRefGoogle Scholar
7. Sostman, HD, Coleman, RE, DeLong, DM, Newman, GE, Paine, S. Evaluation of revised criteria for ventilation-perfusion scintigraphy in patients with suspected pulmonary embolism. Radiology. 1994;193:103–107.CrossRefGoogle ScholarPubMed
8. 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 heptatocellular carcinoma. Control Clin Trials. 2003;24:110–121.CrossRefGoogle Scholar
9. Trueman, P, Lowson, K, Bending, M, Ganderton, M, Chaplin, S, Wright, D, Duffy, S, Saxby, R, Chilcott, J, Tappenden, P, Pilgim, H, Shorthouse, A, Tappenden, J. Bowel cancer services: Costs and benefits. Final Report to the Department of Health 2007. http://www.shef.ac.uk/scharr/sections/heds/modelling/colorectal-cancers.html. Accessed 7 January 2008.Google Scholar
10. 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;25:123–148.CrossRefGoogle ScholarPubMed