Advantages of Using the Net-Benefit Approach for Analysing Uncertainty in Economic Evaluation Studies (original) (raw)

Abstract

No consensus has yet been reached on how to analyse uncertainty in economic evaluation studies where individual patient data are available for costs and health effects. This paper summarises the available results regarding the analysis of uncertainty on the cost-effectiveness plane and argues for using the net-benefit approach when analysing uncertainty in cost-effectiveness studies. The net-benefit approach avoids the interpretation and statistical problems related to the incremental cost effectiveness ratio and implies several advantages. First, traditional statistical methods can be used for confidence-interval estimation and hypothesis testing. Second, calculation of the optimal sample size and the power of the study are facilitated allowing the correlation between costs and effects to vary within and between patient groups. Third, the use of a Bayesian approach to cost-effectiveness analysis is facilitated. Fourth, a formal relation between cost-effectiveness acceptability curves and statistical inference is provided. Finally, the net-benefit approach gives the Fieller’s limits of the confidence interval for the incremental cost-effectiveness ratio in the cost-effectiveness plane. Based on these advantages the net-benefit approach should strongly be considered when analysing uncertainty in cost-effectiveness analyses.

Access this article

Log in via an institution

Subscribe and save

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Briggs A, Fenn P. Confidence intervals or surfaces? Uncertainty on the cost-effectiveness plane. Health Econ 1998; 7 (8): 723–40
    Article CAS Google Scholar
  2. Willan AR, O’Brien BJ. Sample size and power issues in estimating incremental cost-effectiveness ratios from clinical trials data. Health Econ 1999; 8: 203–11
    Article CAS Google Scholar
  3. Briggs A, Gray AM. Power and sample size calculations for stochastic cost-effectiveness analysis. Med Decis Making 1998; 18: 81–92
    Article Google Scholar
  4. Stinnett A, Mullahy J. Net health benefits: a new framework for the analysis of uncertainty in cost-effectiveness analysis. Med Decis Making 1998; 18: 68–80
    Article Google Scholar
  5. Van Hout BA, Al MJ, Gordon GS, et al. Costs, effects and C/E-ratios alongside a clinical trial. Health Econ 1994; 3: 309–19
    Article Google Scholar
  6. Wakker P, Klaassen M. Confidence intervals for cost-effectiveness ratios. Health Econ 1995; 4: 373–81
    Article CAS Google Scholar
  7. Heitjan DF, Moskowitz AJ, Whang W. Bayesian estimation of cost-effectiveness ratios from clinical trials. Health Econ 1999; 8: 191–201
    Article CAS Google Scholar
  8. Jönsson B, Wahlqvist P. Management of NSAID-associated lesions: a cost-effectiveness perspective. Am J Med 1997; 104: 81–8
    Article Google Scholar
  9. Tambour M, Zethraeus N, Johannesson M. A note on confidence intervals in cost-effectiveness analysis. Int J Technol Assess Health Care 1998; 14 (3): 467–71
    Article CAS Google Scholar
  10. Willan AR, Lin DY. Incremental net benefit in randomized clinical trials. Stat Med 2001; 20: 1563–74
    Article CAS Google Scholar
  11. Willan AR. Analysis, sample size, and power for estimating incremental net health benefit from clinical trial data. Control Clin Trials 2001; 22: 228–37
    Article CAS Google Scholar
  12. Löthgren M, Zethraeus N. Definition, interpretation and calculation of cost-effectiveness acceptability curves. Health Econ 2000; 9: 623–30
    Article Google Scholar
  13. Zethraeus N, Löthgren M. On the equivalence of the net benefit and the Fieller’s methods for statistical inference in cost-effectiveness analysis. Working paper no. 379, 2000. SSE/EFI Working Paper Series in Economics and Finance. Stockholm: Stockholm School of Economics, 2000
    Google Scholar
  14. Heitjan DF. Fieller’s method and net health benefits. Health Econ 2000; 9: 327–35
    Article CAS Google Scholar
  15. Stinnett AA. Adjusting for bias in c/e ratio estimates. Health Econ 1996; 5: 470–2
    Article CAS Google Scholar
  16. Laska EM, Meisner M, Siegel C. Power and sample size in cost-effectiveness analysis. Med Decis Making 1999; 19: 339–43
    Article CAS Google Scholar
  17. Stinnett AA. Is it really so bad to be unambiguously inefficient? The role of dominance in stochastic cost-effectiveness analysis. Med Decis Making 1999; 19: 102–3
    Article CAS Google Scholar
  18. Raikou K, Gray A, Briggs A, et al. Cost effectiveness analysis of improved bloodpressure control in hypertensive patients with type 2 diabetes: UKPDS 40. BMJ 1998; 317: 720–6
    Article Google Scholar
  19. Gray A, Raikou M, McGuire A, et al. Cost-effectiveness of an intensive blood glucose control policy in patients with type 2 diabetes: economic analysis alongside randomised controlled trial: UKPDS 41. BMJ 2000; 320: 1373–8
    Article CAS Google Scholar
  20. Briggs A. A Bayesian approach to stochastic cost-effectiveness analysis. Health Econ 1999; 8: 257–61
    Article CAS Google Scholar
  21. O’Hagan A, Stevens JW, Montmartin J. Inference for the cost-effectiveness acceptability curve and cost-effectiveness ratio. Pharmacoeconomics 2000; 17 (4): 339–49
    Article Google Scholar
  22. Briggs A, Tambour M. The design and analysis of stochastic cost-effectiveness studies for the evaluation of health care interventions. Drug Information J 2001; 35; 1455–68
    Article Google Scholar
  23. Claxton K. The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. J Health Econ 1999; 18: 341–64
    Article CAS Google Scholar
  24. Claxton K. Bayesian approaches to the value of information: implications for the regulation of new pharmaceuticals. Health Econ 1999; 8: 269–74
    Article CAS Google Scholar
  25. Obenchain RL, Melfi CA, Croghan TW, et al. Bootstrap analyses of cost effectiveness in antidepressant pharmacotherapy. Pharmacoeconomics 1997; 11 (5): 464–72
    Article CAS Google Scholar
  26. Jönsson B, Cook JR, Pedersen TR. The cost-effectiveness of lipid lowering in patients with diabetes: results from 4S trial. Diabetologia 1999; 42: 1293–301
    Article Google Scholar
  27. Hunink MG, Bult JR, de Vries J, et al. Uncertainty in decision models analyzing cost-effectiveness: the joint distribution of incremental costs and effectiveness evaluated with a nonparametric bootstrap method. Med Decis Making 1998; 18: 337–46
    Article CAS Google Scholar
  28. Halpern EF, Weinstein MC, Hunink MGM, et al. Representing both first- and second-order uncertainties by monte carlo simulation for groups of patients. Med Decis Making 2000; 20: 314–22
    Article CAS Google Scholar

Download references

Acknowledgements

No sources of funding were used to assist in the preparation of this manuscript. The authors have no conflicts of interest that are directly relevant to the content of this manuscript. We are grateful to John Cook for comments on previous versions of this manuscript.

Author information

Authors and Affiliations

  1. Centre for Health Economics, Stockholm School of Economics, P.O Box 6501, S-113 83, Stockholm, Sweden
    Niklas Zethraeus, Magnus Johannesson & Bengt Jönsson
  2. Eli Lilly European Health Outcomes Research, Windlesham, UK
    Mickael Löthgren
  3. Wyeth Lederle Nordiska AB, Solna, Sweden
    Magnus Tambour

Authors

  1. Niklas Zethraeus
  2. Magnus Johannesson
  3. Bengt Jönsson
  4. Mickael Löthgren
  5. Magnus Tambour

Rights and permissions

About this article

Cite this article

Zethraeus, N., Johannesson, M., Jönsson, B. et al. Advantages of Using the Net-Benefit Approach for Analysing Uncertainty in Economic Evaluation Studies.Pharmacoeconomics 21, 39–48 (2003). https://doi.org/10.2165/00019053-200321010-00003

Download citation

Keywords