Skip to main content
Log in

Use of Randomised Controlled Trials for Producing Cost-Effectiveness Evidence

Potential Impact of Design Choices on Sample Size and Study Duration

  • Original Research Article
  • Published:
PharmacoEconomics Aims and scope Submit manuscript

Abstract

Background: A number of approaches to conducting economic evaluations could be adopted. However, some decision makers have a preference for wholly stochastic cost-effectiveness analyses, particularly if the sampled data are derived from randomised controlled trials (RCTs). Formal requirements for cost-effectiveness evidence have heightened concerns in the pharmaceutical industry that development costs and times might be increased if formal requirements increase the number, duration or costs of RCTs. Whether this proves to be the case or not will depend upon the timing, nature and extent of the cost-effectiveness evidence required.

Objective: To illustrate how different requirements for wholly stochastic cost-effectiveness evidence could have a significant impact on two of the major determinants of new drug development costs and times, namely RCT sample size and study duration.

Design: Using data collected prospectively in a clinical evaluation, sample sizes were calculated for a number of hypothetical cost-effectiveness study design scenarios. The results were compared with a baseline clinical trial design.

Results: The sample sizes required for the cost-effectiveness study scenarios were mostly larger than those for the baseline clinical trial design. Circumstances can be such that a wholly stochastic cost-effectiveness analysis might not be a practical proposition even though its clinical counterpart is. In such situations, alternative research methodologies would be required. For wholly stochastic cost-effectiveness analyses, the importance of prior specification of the different components of study design is emphasised. However, it is doubtful whether all the information necessary for doing this will typically be available when product registration trials are being designed.

Conclusions: Formal requirements for wholly stochastic cost-effectiveness evidence based on the standard frequentist paradigm have the potential to increase the size, duration and number of RCTs significantly and hence the costs and timelines associated with new product development. Moreover, it is possible to envisage situations where such an approach would be impossible to adopt. Clearly, further research is required into the issue of how to appraise the economic consequences of alternative economic evaluation research strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Table I
Table II
Table III
Table IV
Table V
Table VI
Table VII
Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Drummond MF, Dubois D, Garattini L, et al. Current trends in the use of pharmacoeconomics and outcomes research in Europe. Value Health 1999; 2: 323–32

    Article  PubMed  CAS  Google Scholar 

  2. National Institute for Clinical Excellence. Technical guidance for manufacturers and sponsors on making a submission to a technology appraisal. 2001 Mar. Available from URL: http://www.nice.org.uk. [Accessed 2001 Apr 15]

  3. O’Brien BJ, Drummond MF, Labelle RJ, et al. In search of power and significance: issues in the design and analysis of stochastic cost-effectiveness studies in health care. Med Care 1994; 32: 150–63

    Article  PubMed  Google Scholar 

  4. Buxton M, Drummond MF, Van Hout BA, et al. Modelling in economic evaluation: an unavoidable fact of life. Health Econ 1997; 6: 217–27

    Article  PubMed  CAS  Google Scholar 

  5. Armitage P, Berry G. Statistical methods in medical research. Oxford: Blackwell Science, 1995

    Google Scholar 

  6. Friedman LM, Furberg CD, DeMets DL. Fundamentals of clinical trials. Littleton (MA): PSG Publishing Company, 1985

    Google Scholar 

  7. Pocock SJ. Clinical trials: a practical approach. Chichester: John Wiley & Sons, 1991

    Google Scholar 

  8. Machin DM, Campbell MJ, Fayers PM, et al. Sample size tables for clinical studies. Oxford: Blackwell Science Ltd, 1997

    Google Scholar 

  9. Rosner B. Fundamentals of biostatistics. Pacific Grove (CA): Duxbury Press, 2000

    Google Scholar 

  10. Briggs A, Gray A. Power and sample size calculations for stochastic cost-effectiveness analysis. Med Decis Making 1998; 18: S81–92

    Article  PubMed  CAS  Google Scholar 

  11. Al MJ, VanHout BA, Michel BC, et al. Sample size calculation in economic evaluations. Health Econ 1998; 7: 327–35

    Article  PubMed  CAS  Google Scholar 

  12. Briggs A, Tambour M. The design and analysis of stochastic cost-effectiveness studies for the evaluation of health care interventions. Stockholm School of Economics Working Paper Series in Economics and Finance 1998; 234: 1–22

    Google Scholar 

  13. Laska EM, Meisner M, Siegel CS. Power and sample size in cost-effectiveness analysis. Med Decis Making 1999; 19: 339–43

    Article  PubMed  CAS  Google Scholar 

  14. Gardiner JC, Huebner M, Jetton J, et al. Power and sample size assessments for tests of hypotheses on cost-effectiveness ratios. Health Econ 2000; 9: 227–34

    Article  PubMed  CAS  Google Scholar 

  15. Stinnett AA, Mullahy J. Net health benefits: a new framework for the analysis of uncertainty in cost-effectiveness analysis. Med Decis Making 1998; 18: S68–80

    Article  PubMed  CAS  Google Scholar 

  16. Fenn P, McGuire A, Phillips V, et al. The analysis of censored treatment cost data in economic evaluation. Med Care 1995; 33: 851–63

    Article  PubMed  CAS  Google Scholar 

  17. Fenn P, McGuire A, Backhouse ME, et al. Modelling programme costs in economic evaluation. J Health Econ 1996; 15: 115–25

    Article  PubMed  CAS  Google Scholar 

  18. Briggs A, Wonderling D. Pulling cost-effectiveness analysis up by its bootstraps: a non-parametric approach to confidence interval estimation. Health Econ 1997; 6: 327–40

    Article  PubMed  CAS  Google Scholar 

  19. Briggs A, Gray A. The distribution of health care costs and their statistical analysis for economic evaluation. J Health Serv Res Policy 1998; 3: 233–45

    PubMed  CAS  Google Scholar 

  20. NHS Management Executive. TFR returns. Leeds: Department of Health, 1999

    Google Scholar 

  21. Smith DH, Gravelle H. The practice of discounting in economic evaluations of healthcare interventions. Int J Technol Assess Health Care 2001; 17: 236–43

    Article  PubMed  CAS  Google Scholar 

  22. Etzioni R, Feuer E, Sullivan S, et al. On the use of survival analysis techniques to estimate medical care costs. J Health Econ 1999; 18: 365–80

    Article  PubMed  CAS  Google Scholar 

  23. Gardiner J, Hogan A, Holmes-Rovner M, et al. Confidence intervals for cost-effectiveness ratios. Med Decis Making 1995; 15: 254–63

    Article  PubMed  CAS  Google Scholar 

  24. Gardiner J, Holmes-Rovner M, Goddeeris J, et al. Covariate-adjusted cost-effectiveness ratios. J Stat Plan Inference 1999; 75: 291–304

    Article  Google Scholar 

  25. Rittenhouse BE, Dulisse B, Stinnett AA. At what price significance? The effect of price estimates on statistical inference in economic evaluation. Health Econ 1999; 8: 213–9

    Article  PubMed  CAS  Google Scholar 

  26. 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  PubMed  CAS  Google Scholar 

  27. Backhouse ME, Mauskopf J. Formal requirements for economic analysis of medicines: the potential implications of the UK NICE requirements for global product development. Boston (MA): Decision Resources, 2000: 6.1–6.14

    Google Scholar 

  28. Pharmaceutical Information Cost Assessment Service (PICAS). Fast track systems inc. Available from URL: http://www.fast-track.com [Accessed 2002 Aug 27]

  29. Backhouse ME. An investment appraisal approach to clinical trial design. Health Econ 1998; 7: 605–19

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

I am grateful to Paul Fenn, Andy Briggs and three anonymous referees who provided helpful comments on earlier versions of this paper. The work was partly funded by a research grant from the British Pharma Group.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Martin E. Backhouse.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Backhouse, M.E. Use of Randomised Controlled Trials for Producing Cost-Effectiveness Evidence. Pharmacoeconomics 20, 1061–1077 (2002). https://doi.org/10.2165/00019053-200220150-00003

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2165/00019053-200220150-00003

Keywords

Navigation