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Handling uncertainty in economic evaluations of patient level data: A review of the use of Bayesian methods to inform health technology assessments

Published online by Cambridge University Press:  22 October 2009

C. Elizabeth McCarron ,
Eleanor M. Pullenayegum ,
Deborah A. Marshall ,
Ron Goeree  and
Jean-Eric Tarride
C. Elizabeth McCarron
Affiliation:
McMaster University
Eleanor M. Pullenayegum
Affiliation:
McMaster University and St. Joseph's Healthcare Hamilton
Deborah A. Marshall
Affiliation:
University of Calgary
Ron Goeree
Affiliation:
McMaster University and St. Joseph's Hospital Hamilton
Jean-Eric Tarride
Affiliation:
McMaster University
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Abstract

Objectives: Due to potential advantages (e.g., using all available evidence), Bayesian methods have been proposed to assist healthcare decision making. This review provides a detailed description of how Bayesian methods have been applied to economic evaluations of patient level data. The results serve both as a reference and as a means by which to examine the appropriate application of Bayesian methods to inform decision making.

Methods: MEDLINE, EMBASE, and Cochrane Economic Evaluation databases were searched to identify studies, published up to November 2007, meeting three inclusion criteria: (i) the study conducted an economic evaluation, (ii) sampling uncertainty was incorporated using Bayesian methods, (iii) the likelihood function was informed by patient level data from a single source. Data were collected on key study characteristics (e.g., prior distribution, likelihood function, presentation of uncertainty).

Results: The search identified 366 potentially relevant studies, from which 103 studies underwent full-text review. Sixteen studies met the inclusion criteria. Half of the studies used uninformative priors; most studies incorporated the potential dependence between costs and effects, and presented cost-effectiveness acceptability curves. Results were sensitive to changes in the priors and likelihoods.

Conclusions: Limited use of informative priors, among the included studies, gives policy makers little guidance on one of the main benefits of Bayesian methods, the ability to integrate all available evidence to capture the uncertainty inherent in decision making.

Keywords

Bayes theoremCost-benefit analysisUncertainty
Type
General Essays
Information
International Journal of Technology Assessment in Health Care , Volume 25 , Issue 4 , October 2009 , pp. 546 - 554
Copyright
Copyright © Cambridge University Press 2009

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References

REFERENCES

1. Al, MJ, Van Hout, BA. A Bayesian approach to economic analyses of clinical trials: the case of stenting versus balloon angioplasty. Health Econ. 2000;9:599–609.3.0.CO;2-#>CrossRefGoogle ScholarPubMed
2. Ashby, D. Bayesian statistics in medicine: A 25 year review. Stat Med. 2006;25:3589–3631.CrossRefGoogle ScholarPubMed
3. Bachmann, MO, Fairall, L, Clark, A, Mugford, M. Methods for analyzing cost effectiveness data from cluster randomized trials. Cost Eff Resour Alloc. 2007;5:12.CrossRefGoogle ScholarPubMed
4. Briggs, AH. A Bayesian approach to stochastic cost-effectiveness analysis: An illustration and application to blood pressure control in type 2 diabetes. Int J Technol Assess Health Care. 2001;17:69–82.CrossRefGoogle ScholarPubMed
5. Briggs, AH. handling uncertainty in cost-effectiveness models. Pharmacoeconomics. 2000;17:479–500.CrossRefGoogle ScholarPubMed
6. Drummond, MF, Sculpher, MJ, Torrance, GW, O'Brien, BJ, Stoddart, GL. Methods for the economic evaluation of health care programmes. 3rd ed. Oxford: Oxford University Press; 2005.CrossRefGoogle Scholar
7. Fenwick, E, Wilson, J, Sculpher, M, Claxton, K. Pre-operative optimisation employing dopexamine or adrenaline for patients undergoing major elective surgery: a cost-effectiveness analysis. Intensive Care Med. 2002;28:599–608.CrossRefGoogle ScholarPubMed
8. Fryback, DG, Chinnis, JO, Ulvila, JW. Bayesian cost-effectiveness analysis: An example using the GUSTO trial. Int J Technol Assess Health Care. 2001;17:83–97.CrossRefGoogle ScholarPubMed
9. Hahn, S, Whitehead, A. An illustration of the modelling of cost and efficacy data from a clinical trial. Stat Med. 2003;22:1009–1024.CrossRefGoogle ScholarPubMed
10. Heitjan, DF, Kim, CY, Li, H. Bayesian estimation of cost-effectiveness from censored data. Stat Med. 2004;23:1297–1309.CrossRefGoogle ScholarPubMed
11. Heitjan, DF, Li, H. Bayesian estimation of cost-effectiveness: An importance-sampling approach. Health Econ. 2004;13:191–198.CrossRefGoogle ScholarPubMed
12. Heitjan, DF, Moskowitz, AJ, Whang, W. Bayesian estimation of cost-effectiveness ratios from clinical trials. Health Econ. 1999;8:191–201.3.0.CO;2-R>CrossRefGoogle ScholarPubMed
13. Johnson-Masotti, AP, Laud, PW, Hoffmann, RG, Hayat, MJ, Pinkerton, SD. Probabilistic cost-effectiveness analysis of HIV prevention comparing a Bayesian approach with traditional deterministic sensitivity analysis. Eval Rev. 2001;25:474–502.CrossRefGoogle ScholarPubMed
14. Negrin, MA, Vazquez-Polo, FJ. Bayesian cost-effectiveness analysis with two measures of effectiveness: The cost-effectiveness acceptability plane. Health Econ. 2006;15:363–372.CrossRefGoogle ScholarPubMed
15. O'Hagan, A, Luce, BR. A primer on Bayesian statistics in health economics and outcomes research. Bayesian Initiative in Health Economics & Outcomes Research Centre for Bayesian Statistics in Health Economics; 2003.Google Scholar
16. O'Hagan, A, Stevens, JW. A framework for cost-effectiveness analysis from clinical trial data. Health Econ. 2001;10:303–315.CrossRefGoogle ScholarPubMed
17. O'Hagan, A, Stevens, JW. Bayesian methods for design and analysis of cost-effectiveness trials in the evaluation of health care technologies. Stat Methods Med Res. 2002;11:469–490.CrossRefGoogle ScholarPubMed
18. O'Hagan, A, Stevens, JW, Montmartin, J. Bayesian cost-effectiveness analysis from clinical trial data. Stat Med. 2001;20:733–753.CrossRefGoogle ScholarPubMed
19. Ramsey, S, Willke, R, Briggs, A, et al. Good research practices for cost-effectiveness analysis alongside clinical trials: The ISPOR RCT-CEA Task Force Report. Value Health. 2005;8:521–533.CrossRefGoogle ScholarPubMed
20. Shih, YC, Bekele, NB, Xu, Y. Use of Bayesian net benefit regression model to examine the impact of generic drug entry on the cost effectiveness of selective serotonin reuptake inhibitors in elderly depressed patients. Pharmacoeconomics. 2007;25:843–862.CrossRefGoogle ScholarPubMed
21. Skrepnek, GH. The contrast and convergence of Bayesian and frequentist statistical approaches in pharmacoeconomic analysis. Pharmacoeconomics. 2007;25:649–664.CrossRefGoogle ScholarPubMed
22. Spiegelhalter, DJ, Abrams, KR, Myles, JP. Bayesian approaches to clinical trials and health-care evaluation. Chichester, West Sussex: John Wiley & Sons Ltd; 2004.Google Scholar
23. Stevens, JW, O'Hagan, A. Incorporation of genuine prior information in cost-effectiveness analysis of clinical trial data. Int J Technol Assess Health Care. 2002;18:782–790.CrossRefGoogle ScholarPubMed
24. Sung, L, Hayden, J, Greenberg, ML, et al. Seven items were identified for inclusion when reporting a Bayesian analysis of a clinical study. J Clin Epidemiol. 2005;58:261–268.CrossRefGoogle ScholarPubMed
25. The BaSiS Group. Bayesian standards in science (BaSiS). http://lib.stat.cmu.edu/bayesworkshop/2001/BaSis.html. 2001.Google Scholar
26. UK BEAM Trial Team. United Kingdom back pain exercise and manipulation (UKBEAM) randomised trial: cost effectiveness of physical treatments for back pain in primary care. BMJ. 2004;329:1381–1385.CrossRefGoogle Scholar
27. Vazquez-Polo, FJ, Hernandez, MAN, Lopez-Valcarcel, BG. Using covariates to reduce uncertainty in the economic evaluation of clinical trial data. Health Econ. 2005;14:545–557.CrossRefGoogle ScholarPubMed
28. Vazquez-Polo, FJ, Negrin, M, Badia, X, Roset, M. Bayesian regression models for cost-effectiveness analysis. Eur J Health Econ. 2005;6:45–52.CrossRefGoogle Scholar
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