Skip to main content
SpringerLink
Log in

Statistical Analysis of Clinical Trial Data for Resource Allocation Decisions

  • Reference work entry
  • First Online:
The New Palgrave Dictionary of Economics

  • 12 Accesses

Abstract

Randomised controlled trials (RCTs) are a major source of individual patient level data (IPD) on the clinical outcomes, costs and other measures of health consequences associated with alternative healthcare interventions. These data are typically used to establish the ‘value for money’ of healthcare technologies. While a clear policy framework to guide the above assessment exists, the analysis of RCT data to inform decision making requires the adoption of a specific quantitative methodological framework. This article describes the use of RCTs for cost-effectiveness analysis, discusses the major statistical issues and possible solutions, and outlines recent research developments in this area.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 6,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 8,499.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Bibliography

  • Ades, A.E., M. Sculpher, A. Sutton, K. Abrams, N. Cooper, N. Welton, and G. Lu. 2006. Bayesian methods for evidence synthesis in cost-effectiveness analysis. PharmacoEconomics 24: 1–19.

    Article  Google Scholar 

  • Angrist, J.D. 2012. Treatment effect. In The new Palgrave dictionary of economics, ed. S.N. Durlauf and L.E. Blume. Basingstoke: Palgrave Macmillan.

    Google Scholar 

  • Austin, P.C. 2002. A comparison of methods for analyzing health-related quality-of-life measures. Value in Health 5: 329–337.

    Article  Google Scholar 

  • Austin, P.C., M. Escobar, and J.A. Kopec. 2000. The use of the Tobit model for analyzing measures of health status. Quality of Life Research 9: 901–910.

    Article  Google Scholar 

  • Basu, A. 2011. Economics of individualization in comparative effectiveness research and a basis for a patient-centered health care. Journal of Health Economics 30(3): 549–559.

    Article  Google Scholar 

  • Basu, A., and P.J. Rathouz. 2005. Estimating marginal and incremental effects on health outcomes using flexible link and variance function models. Biostatistics 6: 93–109.

    Article  Google Scholar 

  • Basu, A., and D. Meltzer. 2007. Value of information on preference heterogeneity and individualized care. Medical Decision Making 27: 112–127.

    Article  Google Scholar 

  • Basu, A., and A. Manca. 2012. Regression estimators for generic health-related quality of life and quality-adjusted life years. Medical Decision Making 32: 56–69.

    Article  Google Scholar 

  • Black, W.C. 1990. The CE plane. Medical Decision Making 10: 212–214.

    Article  Google Scholar 

  • Box, G.E.P., and D.R. Cox. 1964. An analysis of transformations. Journal of the Royal Statistical Society: Series B: Methodological 26(2): 211–252.

    Google Scholar 

  • Briggs, A.H., D.E. Wonderling, and C.Z. Mooney. 1997. Pulling cost-effectiveness analysis up by its bootstraps: A non-parametric approach to confidence interval estimation. Health Economics 6: 327–340.

    Article  Google Scholar 

  • Briggs, A.H., C.Z. Mooney, and D.E. Wonderling. 1999. Constructing confidence intervals for cost-effectiveness ratios: An evaluation of parametric and non-parametric techniques using Monte Carlo simulation. Statistics in Medicine 18: 3245–3262.

    Article  Google Scholar 

  • Briggs, A., R. Nixon, S. Dixon, and S. Thompson. 2005. Parametric modelling of cost data: Some simulation evidence. Health Economics 14: 421–428.

    Article  Google Scholar 

  • Briggs, A.H., K. Claxton, and M. Sculpher. 2006. Decision modelling for health economic evaluation. Oxford: Oxford University Press.

    Google Scholar 

  • Buntin, M.B., and A.M. Zaslavsky. 2004. Too much ado about two-part models and transformation?: Comparing methods of modeling Medicare expenditures. Journal of Health Economics 23: 525–542.

    Article  Google Scholar 

  • Chib, S. 2008. Markov chain Monte Carlo methods. In The new Palgrave dictionary of economics, ed. S.N. Durlauf and L.E. Blume. Basingstoke: Palgrave Macmillan.

    Google Scholar 

  • Coyle, D., M.J. Buxton, and B.J. O’Brien. 2003. Stratified cost-effectiveness analysis: A framework for establishing efficient limited use criteria. Health Economics 12: 421–427.

    Article  Google Scholar 

  • Dolan, P. 2000. The measurement of health-related quality of life for use in resource allocation in health care. In Handbook of health economics, ed. A Culyer and J. Newhouse. Amsterdam: Elsevier.

    Google Scholar 

  • Drummond, M.F., M.J. Sculpher, and G.W. Torrance. 2005. Methods for the economic evaluation of health care programmes. New York: Oxford University Press.

    Google Scholar 

  • Fenwick, E., K. Claxton, and M. Sculpher. 2001. Representing uncertainty: The role of cost-effectiveness acceptability curves. Health Economics 10: 779–787.

    Article  Google Scholar 

  • Fenwick, E., B.J. O’Brien, and A. Briggs. 2004. Cost-effectiveness acceptability curves – Facts, fallacies and frequently asked questions. Health Economics 13: 405–415.

    Article  Google Scholar 

  • Gold, M.R., J.E. Siegel, L.B. Russell, and M.C. Weinstein. 1996. Cost-effectiveness in health and medicine. New York: Oxford University Press.

    Google Scholar 

  • Griffin, S.C., J.A. Barber, A. Manca, M.J. Sculpher, S.G. Thompson, M.J. Buxton, and H. Hemingway. 2007. Cost effectiveness of clinically appropriate decisions on alternative treatments for angina pectoris: Prospective observational study. British Medical Journal 334: 624.

    Article  Google Scholar 

  • Hernandez Alava, M., A.J. Wailoo, and R. Ara. 2012. Tails from the peak district: Adjusted limited dependent variable mixture models of EQ-5D questionnaire health state utility values. Value in Health 15: 550–561.

    Article  Google Scholar 

  • Hoch, J.S., A.H. Briggs, and A.R. Willan. 2002. Something old, something new, something borrowed, something blue: A framework for the marriage of health econometrics and cost-effectiveness analysis. Health Economics 11: 415–430.

    Article  Google Scholar 

  • Huang, I., C. Frangakis, M.J. Atkinson, R.J. Willke, W.L. Leite, W.B. Vogel, and A.W. Wu. 2008. Addressing ceiling effects in health status measures: A comparison of techniques applied to measures for people with HIV disease. Health Services Research 43: 327–339.

    Article  Google Scholar 

  • Jones, A.M. 2010. Models for health care. York: Centre for Health Economics, University of York.

    Google Scholar 

  • Jones, A.M. 2012. Health econometrics. In The new Palgrave dictionary of economics, ed. S.N. Durlauf and L.E. Blume. Basingstoke: Palgrave Macmillan.

    Google Scholar 

  • Keene, O.N. 2007. The log transformation is special. Statistics in Medicine 14: 811–819.

    Article  Google Scholar 

  • Kihlberg, J.K., J.H. Herson, and W.E. Schotz. 1972. Square root transformation revisited. Applied Statistics 21: 76–81.

    Article  Google Scholar 

  • Lindsay, B.G., and M. Stewart. 2008. Mixture models. In The new Palgrave dictionary of economics, ed. S.N. Durlauf and L.E. Blume. Basingstoke: Palgrave Macmillan.

    Google Scholar 

  • Lu, G., and A.E. Ades. 2004. Combination of direct and indirect evidence in mixed treatment comparisons. Statistical Medicine 23: 3105–3124.

    Article  Google Scholar 

  • Manca, A., N. Hawkins, and M.J. Sculpher. 2005a. Estimating mean QALYs in trial-based cost-effectiveness analysis: The importance of controlling for baseline utility. Health Economics 14: 487–496.

    Article  Google Scholar 

  • Manca, A., N. Rice, M.J. Sculpher, and A.H. Briggs. 2005b. Assessing generalisability by location in trial-based cost-effectiveness analysis: The use of multilevel models. Health Economics 14: 471–485.

    Article  Google Scholar 

  • Manca, A., and A.R. Willan. 2006. ‘Lost in translation’: Accounting for between-country differences in the analysis of multinational cost-effectiveness data. PharmacoEconomics 24: 1101.

    Article  Google Scholar 

  • Matthews, J.N.S. 2006. Introduction to randomized controlled clinical trials. London: Chapman & Hall/CRC.

    Book  Google Scholar 

  • McKenna, C., and K. Claxton. 2011. Addressing adoption and research design decisions simultaneously. Medical Decision Making 31: 853–865.

    Article  Google Scholar 

  • Mihaylova, B., A. Briggs, A. O’Hagan, and S.G. Thompson. 2011. Review of statistical methods for analysing healthcare resources and costs. Health Economics 20: 897–916.

    Article  Google Scholar 

  • Mullahy, J. 1998. Much ado about two: Reconsidering retransformation and the two-part model in health econometrics. Journal of Health Economics 17: 247–281.

    Article  Google Scholar 

  • Nelder, J.A., and R.W.M. Wedderburn. 1972. Generalized linear models. Journal of the Royal Statistical Society, Series A (General) 135(3): 370–384.

    Article  Google Scholar 

  • Nixon, R.M., and S.G. Thompson. 2005. Methods for incorporating covariate adjustment, subgroup analysis and between-centre differences into cost-effectiveness evaluations. Health Economics 14: 1217–1229.

    Article  Google Scholar 

  • Polsky, D., H.A. Glick, R. Willke, and K. Schulman. 1997. Confidence intervals for cost-effectiveness ratios: A comparison of four methods. Health Economics 6: 243–252.

    Article  Google Scholar 

  • Powell, J.L. 2008. Semiparametric estimation. In The new Palgrave dictionary of economics, ed. S.N. Durlauf and L.E. Blume. Basingstoke: Palgrave Macmillan.

    Google Scholar 

  • Pullenayegum, E.M., Wong, H.S., and A. Childs. 2012. Generalized additive models for the analysis of EQ-5D utility data. Medical Decision Making (in press).

    Google Scholar 

  • Quinn, C. 2007. The health-economic applications of copulas: Methods in applied econometric research. Health, Econometrics and Data Group (HEDG) Working Paper, 7, 22.

    Google Scholar 

  • Rabin, R., and F. Charro. 2001. EQ-SD: A measure of health status from the EuroQol Group. Annals of Medicine 33: 337–343.

    Article  Google Scholar 

  • Robin, J.-M. 2008. Tobit model. In The new Palgrave dictionary of economics, ed. S.N. Durlauf and L.E. Blume. Basingstoke: Palgrave Macmillan.

    Google Scholar 

  • Saramago, P., A.J. Sutton, N.J. Cooper, and A. Manca. 2012. Mixed treatment comparisons using aggregate and individual participant level data. Statistics in Medicine 31(28): 3516–3536.

    Article  Google Scholar 

  • Sculpher, M. 2008. Subgroups and heterogeneity in cost-effectiveness analysis. PharmacoEconomics 26: 799–806.

    Article  Google Scholar 

  • Sculpher, M.J., K. Claxton, M. Drummond, and C. McCabe. 2006. Whither trial-based economic evaluation for health care decision making? Health Economics 15: 677–687.

    Article  Google Scholar 

  • Sekhon, J.S., and R.D. Grieve. 2011. A matching method for improving covariate balance in cost-effectiveness analyses. Health Economics 21: 695–714.

    Article  Google Scholar 

  • Stinnett, A.A., and J. Mullahy. 1998. Net health benefits. Medical Decision Making 18: S68–S80.

    Article  Google Scholar 

  • Sutton, A.J., K.R. Abrams, A.E. Ades, N.J. Cooper, and N.J. Welton. 2012. Evidence synthesis for decision making in healthcare. Chichester: Wiley.

    Google Scholar 

  • Tambour, M., N. Zethraeus, and M. Johannesson. 1998. A note on confidence intervals in cost-effectiveness analysis. International Journal of Technology Assessment in Health Care 14: 467–471.

    Article  Google Scholar 

  • Thompson, S.G., and J.A. Barber. 2000. How should cost data in pragmatic randomised trials be analysed? British Medical Journal 320: 1197.

    Article  Google Scholar 

  • Torrance, G.W., D.H. Feeny, W.J. Furlong, R.D. Barr, Y. Zhang, and Q. Wang. 1996. Multiattribute utility function for a comprehensive health status classification system: Health Utilities Index Mark 2. Medical Care 34: 702.

    Article  Google Scholar 

  • Trivedi, P.K. 2008. Copulas. In The new Palgrave dictionary of economics, ed. S.N. Durlauf and L.E. Blume. Basingstoke: Palgrave Macmillan.

    Google Scholar 

  • Trivedi, P.K., and D.M. Zimmer. 2005. Copula modeling: An introduction for practitioners. Foundations and Trends in Econometrics 1: 1–111.

    Article  Google Scholar 

  • Tunis, S.R., D.B. Stryer, and C.M. Clancy. 2003. Practical clinical trials. Journal of the American Medical Association 290: 1624–1632.

    Google Scholar 

  • Van Hout, B.A., M.J. Al, G.S. Gordon, and F.F.H. Rutten. 1994. Costs, effects and c/e-ratios alongside a clinical trial. Health Economics 3: 309–319.

    Article  Google Scholar 

  • Vanness, D.J., and J. Mullahy. 2012. Perspectives on mean-based evaluation of health care. In The elgar companion to health economics, ed. A.M. Jones. Cheltenham: Edward Elgar.

    Google Scholar 

  • Willan, A.R., A.H. Briggs, and J.S. Hoch. 2004. Regression methods for covariate adjustment and subgroup analysis for non-censored cost-effectiveness data. Health Economics 13: 461–475.

    Article  Google Scholar 

  • Zellner, A., and D.S. Huang. 1962. Further properties of efficient estimators for seemingly unrelated regression equations. International Economic Review 3: 300–313.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. http://link.springer.com/referencework/10.1057/978-1-349-95121-5

    Rita Faria & Andrea Manca

Authors
  1. Rita Faria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea Manca
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Copyright information

© 2018 Macmillan Publishers Ltd.

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Faria, R., Manca, A. (2018). Statistical Analysis of Clinical Trial Data for Resource Allocation Decisions. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2868

Download citation

Publish with us

Policies and ethics

Search

Navigation