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An Educational Review of the Statistical Issues in Analysing Utility Data for Cost-Utility Analysis

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Abstract

The aim of cost-utility analysis is to support decision making in healthcare by providing a standardised mechanism for comparing resource use and health outcomes across programmes of work. The focus of this paper is the denominator of the cost-utility analysis, specifically the methodology and statistical challenges associated with calculating QALYs from patient-level data collected as part of a trial. We provide a brief description of the most common questionnaire used to calculate patient level utility scores, the EQ-5D, followed by a discussion of other ways to calculate patient level utility scores alongside a trial including other generic measures of health-related quality of life and condition- and population-specific questionnaires. Detail is provided on how to calculate the mean QALYs per patient, including discounting, adjusting for baseline differences in utility scores and a discussion of the implications of different methods for handling missing data. The methods are demonstrated using data from a trial. As the methods chosen can systematically change the results of the analysis, it is important that standardised methods such as patient-level analysis are adhered to as best as possible. Regardless, researchers need to ensure that they are sufficiently transparent about the methods they use so as to provide the best possible information to aid in healthcare decision making.

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Notes

  1. Original follow-up points for trial data were 3, 6 and 9 months. These have been extended so as to demonstrate the impact of discounting which only occurs after 1 year.

  2. Note that some issues with the ceiling effect seen in the EQ-5D have been overcome by the development of a 5-level version of the questionnaire [38].

References

  1. 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.

    Google Scholar 

  2. Torrance GW, Feeny D. Utilities and quality adjusted life years. Int J Technol Assess. 1989;5:559–75.

    Article  CAS  Google Scholar 

  3. Richardson G, Manca A. Calculation of quality adjusted life years in the published literature: a review of methodology and transparency. Health Econ. 2004;13:1203–10.

    Article  PubMed  Google Scholar 

  4. National Institute for Health and Care Excellence (NICE). Guide to the methods of technology appraisal 2013. NICE. 2013. http://publications.nice.org.uk/pmg9. Accessed 24 Mar 2014.

  5. Oemar M, Oppe M. EQ-5D-3L user guide. Basic information on how to use the EQ-5D-3L instrument. Version 5.0. EuroQol. 2013. http://www.euroqol.org/fileadmin/user_upload/Documenten/PDF/Folders_Flyers/EQ-5D-3L_UserGuide_2013_v5.0_October_2013.pdf. Accessed 24 Mar 2014.

  6. Dolan P. Modelling valuations for EuroQol health states. Med Care. 1997;35:1095–108.

    Article  CAS  PubMed  Google Scholar 

  7. Oemar M, Janssen B. EQ-5D-5L user guide. Basic information on how to use the EQ-5D-5L instrument. Version 2.0. EuroQol. 2013. http://www.euroqol.org/fileadmin/user_upload/Documenten/PDF/Folders_Flyers/UserGuide_EQ-5D-5L_v2.0_October_2013.pdf. Accessed 24 Mar 2014.

  8. Brazier JE, Rowen D, Mavranezouli I, Tsuchiya A, Young T, Yang Y, et al. Developing and testing methods for deriving preference-based measures of health from condition-specific measures (and other patient-based measures of outcome). Health Technol Assess. 2012;16:1–114.

    Article  CAS  Google Scholar 

  9. Longworth L, Yang Y, Young T, Mulhern B, Hernández Alava M, Mukuria C, et al. Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: a systematic review, statistical modelling and survey. Health Technol Assess. 2014;18:1–224.

    Article  PubMed  Google Scholar 

  10. Brazier J, Roberts J, Deverill M. The estimation of a preference-based measure of health from the SF-36. J Health Econ. 2002;21:271–92.

    Article  PubMed  Google Scholar 

  11. Feeny D, Furlong W, Boyle M, Torrance GW. Multi-attribute health status classification systems: Health Utilities Index. Pharmacoeconomics. 1995;7:490–502.

    Article  CAS  PubMed  Google Scholar 

  12. Stevens K, McCabe CJ, Brazier JE, Roberts J. Multi-attribute utility function or statistical inference models: a comparison of health state valuation models using the HUI2 health state classification system. J Health Econ. 2007;26:992–1002.

    Article  PubMed  Google Scholar 

  13. Stevens K. Valuation of the Child Health Utility 9D Index. Pharmacoeconomics. 2012;30:729–47.

    Article  PubMed  Google Scholar 

  14. Rowen D, Brazier J, Young T, Gaugris S, Craig BM, King MT, et al. Deriving a preference-based measure for cancer using the EORTC QLQ-C30. Value Health. 2011;14:721–31.

    Article  PubMed  Google Scholar 

  15. Mulhern B, Rowen D, Jacoby A, Marson T, Snape D, Hughes D, et al. The development of a QALY measure for epilepsy: NEWQOL-6D. Epilepsy Behav. 2012;24:36–43.

    Article  PubMed  Google Scholar 

  16. Smith SC, Lamping DL, Banerjee S, Harwood R, Foley B, Smith P, et al. Measurement of health-related quality of life for people with dementia: development of a new instrument (DEMQOL) and an evaluation of current methodology. Health Technol Assess. 2005;9:1–93.

    CAS  Google Scholar 

  17. Mulhern B, Rowen D, Brazier J, Smith S, Romeo R, Tait R, et al. Development of DEMQOL-U and DEMQOL-PROXY-U: generation of preference-based indices from DEMQOL and DEMQOL-PROXY for use in economic evaluation. Health Technol Assess. 2013;17:1–140.

    Article  Google Scholar 

  18. Bryan S, Hardyman W, Bentham P, Buckley A, Laight A. Proxy completion of EQ-5D in patients with dementia. Qual Life Res. 2005;14:107–18.

    Article  CAS  PubMed  Google Scholar 

  19. Kontodimopoulos N, Aletras VH, Paliouras D, Niakas D. Mapping the cancer-specific EORTC QLQ-C30 to the preference-based EQ-5D, SF-6D, and 15D instruments. Value Health. 2009;12:1151–7.

    Article  PubMed  Google Scholar 

  20. Kaambwa B, Billingham L, Bryan S. Mapping utility scores from the Barthel index. Eur J Health Econ. 2013;14:231–41.

    Article  PubMed  Google Scholar 

  21. Longworth L, Rowen D. Mapping to obtain EQ-5D utility values for use in NICE health technology assessments. Value Health. 2013;16:202–10.

    Article  PubMed  Google Scholar 

  22. Brazier JE, Rowen D, Mavranezouli I, Tsuchiya A, Young T, Yang Y, et al. Developing and testing methods for deriving preference-based measures of health from condition-specific measures (and other patient-based measures of outcome). Health Technol Assess. 2012;16:1–132.

    Article  CAS  Google Scholar 

  23. Cooper SA, Morrison J, Allan LM, McConnachie A, Greenlaw N, Melville CA, et al. Practice nurse health checks for adults with intellectual disabilities: a cluster-design, randomised controlled trial. Lancet Psychiatry. 2014;7:511–21.

    Article  Google Scholar 

  24. Miyamoto JM. Quality-adjusted life years (QALY) utility models under expected utility and rank-dependent utility assumptions. J Math Psychol. 1999;43:201–37.

    Article  PubMed  Google Scholar 

  25. Billingham LJ, Abrams KR, Jones DR. Methods for the analysis of quality-of-life and survival data in health technology assessment. Health Technol Assess. 1999;3:1–152.

    CAS  PubMed  Google Scholar 

  26. Whitehurst DG, Bryan S, Lewis M. Systematic review and empirical comparison of contemporaneous EQ-5D and SF-6D group mean scores. Med Decis Making. 2011;31:E34–44.

    Article  PubMed  Google Scholar 

  27. Glick HA, Doshe JA, Sonnad SS, Polsky D. Economic evaluation in clinical trials. 2nd ed. Oxford: Oxford University Press; 2007.

    Google Scholar 

  28. NICE. Incorporating health economics. In: Methods for the development of NICE public health guidance (3rd ed). NICE. 2012. http://publications.nice.org.uk/methods-for-the-development-of-nice-public-health-guidance-third-edition-pmg4/incorporating-health-economics. Accessed 24 Mar 2014.

  29. Committee for Medicinal Products for Human Use (CHMP). Guideline on adjustment for baseline covariates. European Medicines Agency. 2013. http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2013/06/WC500144946.pdf. Accessed 17 June 2014.

  30. Manca A, Hawkins N, Sculpher MJ. Estimating mean QALYs in trial-based cost-effectiveness analysis: the importance of controlling for baseline utility. Health Econ. 2005;14:487–96.

    Article  PubMed  Google Scholar 

  31. Carpenter JR, Kenward MG. Missing data in randomised controlled trials—a practical guide. Birmingham: National Institute for Health Research; 2008. http://missingdata.lshtm.ac.uk/downloads/rm04_jh17_mk.pdf. Accessed 24 Mar 2014.

  32. Brueton VC, Tierney JF, Stenning S, Meredith S, Harding S, Nazareth I, et al. Strategies to improve retention in randomised trials: a Cochrane systematic review and meta-analysis. BMJ Open. 2013;4:e003821.

    Article  Google Scholar 

  33. Little RJ, Rubin DB. Statistical analysis with missing data. 2nd ed. New York: Wiley; 2002.

    Book  Google Scholar 

  34. Noble SM, Hollingworth W, Tilling K. Missing data in trial-based cost-effectiveness analysis: the current state of play. Health Econ. 2012;21:187–200.

    Article  PubMed  Google Scholar 

  35. Eekhout I, de Boer RM, Twisk JW, de Vet HC, Heymans MW. Missing data: a systematic review of how they are reported and handled. Epidemiology. 2012;23:729–32.

    Article  PubMed  Google Scholar 

  36. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30:377–99.

    Article  PubMed  Google Scholar 

  37. Willan AR, Briggs AH, Hoch JS. Regression methods for covariate adjustment and subgroup analysis for non-censored cost-effectiveness data. Health Econ. 2004;13:461–75.

    Article  PubMed  Google Scholar 

  38. Janssen MF, Pickard AS, Golicki D, Gudex C, Niewada M, Scalone L, et al. Measurement properties of the EQ-5D-5L compared to EQ-5D-3L across eight patient groups: a multi country study. Qual Life Res. 2012;22:1717–27.

    Article  PubMed Central  PubMed  Google Scholar 

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Acknowledgments

We would like to thank Professor Sally-Anne Cooper and colleagues at the University of Glasgow for allowing us to use their data as part of this analysis. The data were amended for demonstration purposes and so that no comparison can be drawn between the results of this analysis and any results of the trial.

Conflict of interest

No funding was received for the analysis or writing of this paper. None of the authors have any conflicts of interest to report.

Individual author contributions

All authors contributed to the original idea for the paper and have provided written contributions to the paper including edits to draft versions. RMH wrote the original draft, conducted data analysis and coordinated additional edits to the paper. GB contributed to the section on missing data in addition to comments and edits on the paper. TB contributed to sections on alternatives to the EQ-5D in addition to comments and edits on the paper. NF, SM and JR provided expertise on the overall ideas and content of the paper in addition to edits and comments on each draft of the paper.

RMH will act as overall guarantor for the work.

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Authors and Affiliations

  1. Research Department of Primary Care and Population Health, University College London Medical School (UCL), Royal Free Campus, Rowland Hill Street, London, NW3 2PF, UK

    Rachael Maree Hunter & Nick Freemantle

  2. Department of Statistical Science, UCL, London, UK

    Gianluca Baio

  3. UCL Institute of Ophthalmology, London, UK

    Thomas Butt

  4. Department of Applied Health Research, UCL, London, UK

    Stephen Morris

  5. UCL Clinical Trials Unit, UCL, London, UK

    Jeff Round

Authors
  1. Rachael Maree Hunter
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  2. Gianluca Baio
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  3. Thomas Butt
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  5. Jeff Round
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  6. Nick Freemantle
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Correspondence to Rachael Maree Hunter.

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Hunter, R.M., Baio, G., Butt, T. et al. An Educational Review of the Statistical Issues in Analysing Utility Data for Cost-Utility Analysis. PharmacoEconomics 33, 355–366 (2015). https://doi.org/10.1007/s40273-014-0247-6

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