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PharmacoEconomics

, Volume 36, Issue 9, pp 1043–1061 | Cite as

A Practical Guide to Conducting a Systematic Review and Meta-analysis of Health State Utility Values

  • Stavros Petrou
  • Joseph Kwon
  • Jason Madan
Practical Application

Abstract

Economic analysts are increasingly likely to rely on systematic reviews and meta-analyses of health state utility values to inform the parameter inputs of decision-analytic modelling-based economic evaluations. Beyond the context of economic evaluation, evidence from systematic reviews and meta-analyses of health state utility values can be used to inform broader health policy decisions. This paper provides practical guidance on how to conduct a systematic review and meta-analysis of health state utility values. The paper outlines a number of stages in conducting a systematic review, including identifying the appropriate evidence, study selection, data extraction and presentation, and quality and relevance assessment. The paper outlines three broad approaches that can be used to synthesise multiple estimates of health utilities for a given health state or condition, namely fixed-effect meta-analysis, random-effects meta-analysis and mixed-effects meta-regression. Each approach is illustrated by a synthesis of utility values for a hypothetical decision problem, and software code is provided. The paper highlights a number of methodological issues pertinent to the conduct of meta-analysis or meta-regression. These include the importance of limiting synthesis to ‘comparable’ utility estimates, for example those derived using common utility measurement approaches and sources of valuation; the effects of reliance on limited or poorly reported published data from primary utility assessment studies; the use of aggregate outcomes within analyses; approaches to generating measures of uncertainty; handling of median utility values; challenges surrounding the disentanglement of utility estimates collected serially within the context of prospective observational studies or prospective randomised trials; challenges surrounding the disentanglement of intervention effects; and approaches to measuring model validity. Areas of methodological debate and avenues for future research are highlighted.

Notes

Acknowledgements

We are grateful to departmental colleagues for their comments on the paper and suggestions provided. SP receives financial support as a National Institute for Health Research Senior Investigator. No specific funding was obtained to produce this paper. The authors do not have any conflicts of interest to declare.

Author Contributions

All authors contributed to the conception, design and drafting of the paper. All authors reviewed and approved the final version of the paper. SP is the guarantor of the overall content.

Supplementary material

40273_2018_670_MOESM1_ESM.docx (19 kb)
Supplementary material 1 (DOCX 19 kb)

References

  1. 1.
    PBAC (Pharmaceutical Benefits Advisory Committee). Guidelines for preparing submissions to the pharmaceutical benefits advisory committee. Australia: Australian Government Department of Health; 2013.Google Scholar
  2. 2.
    CADTH (Canadian Agency for Drugs and Technologies in Health). Guidelines for the economic evaluation of health technologies. Canada: CADTH; 2006.Google Scholar
  3. 3.
    HAS (Haute Autorité de Santé). Choices in methods for economic evaluation. France: HAS; 2012.Google Scholar
  4. 4.
    CVZ (College voor zorgverzekeringen). Guidelines for pharmacoeconomic research: evaluation and actualisation. Diemen: CVZ; 2006.Google Scholar
  5. 5.
    CatSalut. Guia I Recomanacions Per A La Realització I Presentació D’avaluacions Econòmiques I Anàlisis D’impacte Pressupostari De Medicaments En L’àmbit Del Catsalut. Catalonia: CatSalut; 2014.Google Scholar
  6. 6.
    NICE (National Institute of Health and Care Excellence). Guide to the methods of technology appraisal. London: NICE; 2013.Google Scholar
  7. 7.
    SMC (Scottish Medicines Consortium). Guidance to manufacturers for completion of new product assessment form (NPAF). Scotland: NHS Scotland; 2016.Google Scholar
  8. 8.
    Brooks R. EuroQol: the current state of play. Health Policy. 1996;37(1):53–72.CrossRefPubMedGoogle Scholar
  9. 9.
    Feeny D, Furlong W, Boyle M, Torrance GW. Multi-attribute health status classification systems. Health Utilities Index. Pharmacoeconomics. 1995;7(6):490–502.CrossRefPubMedGoogle Scholar
  10. 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(2):271–92.CrossRefPubMedGoogle Scholar
  11. 11.
    Brazier J, Ratcliffe J, Salomon J, Tsuchiya A. Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press; 2007.Google Scholar
  12. 12.
    Wolowacz SE, Briggs A, Belozeroff V, Clarke P, Doward L, Goeree R, et al. Estimating health-state utility for economic models in clinical studies: an ISPOR Good Research Practices Task Force Report. Value Health. 2016;19(6):704–19.CrossRefPubMedGoogle Scholar
  13. 13.
    Sculpher MJ, Claxton K, Drummond M, McCabe C. Whither trial-based economic evaluation for health care decision making? Health Econ. 2006;15(7):677–87.CrossRefPubMedGoogle Scholar
  14. 14.
    Tengs TO, Wallace A. One thousand health-related quality-of-life estimates. Med Care. 2000;38(6):583–637.CrossRefPubMedGoogle Scholar
  15. 15.
    Bell CM, Chapman RH, Stone PW, Sandberg EA, Neumann PJ. An off-the-shelf help list: a comprehensive catalog of preference scores from published cost-utility analyses. Med Decis Mak. 2001;21(4):288–94.Google Scholar
  16. 16.
    McLernon DJ, Dillon J, Donnan PT. Health-state utilities in liver disease: a systematic review. Med Decis Mak. 2008;28(4):582–92.CrossRefGoogle Scholar
  17. 17.
    Doth AH, Hansson PT, Jensen MP, Taylor RS. The burden of neuropathic pain: a systematic review and meta-analysis of health utilities. Pain. 2010;149(2):338–44.CrossRefPubMedGoogle Scholar
  18. 18.
    Shearer J, Green C, Ritchie CW, Zajicek JP. Health state values for use in the economic evaluation of treatments for Alzheimer’s disease. Drugs Aging. 2012;29(1):31–43.CrossRefPubMedGoogle Scholar
  19. 19.
    Mohiuddin S, Payne K. Utility values for adults with unipolar depression: Systematic review and meta-analysis. Med Decis Mak. 2014;34(5):666–85.CrossRefGoogle Scholar
  20. 20.
    Djalalov S, Rabeneck L, Tomlinson G, Bremner KE, Hilsden R, Hoch JS. A review and meta-analysis of colorectal cancer utilities. Med Decis Mak. 2014;34(6):809–18.CrossRefGoogle Scholar
  21. 21.
    Tengs TO, Lin TH. A meta-analysis of utility estimates for HIV/AIDS. Med Decis Mak. 2002;22(6):475–81.CrossRefGoogle Scholar
  22. 22.
    Peasgood T, Ward SE, Brazier J. Health-state utility values in breast cancer. Expert Rev Pharmacoecon Outcomes Res. 2010;10(5):553–66.CrossRefPubMedGoogle Scholar
  23. 23.
    Beaudet A, Clegg J, Thuresson PO, Lloyd A, McEwan P. Review of utility values for economic modeling in type 2 diabetes. Value Health. 2014;17(4):462–70.CrossRefPubMedGoogle Scholar
  24. 24.
    Gheorghe A, Moran G, Duffy H, Roberts T, Pinkney T, Calvert M. Health utility values associated with surgical site infection: a systematic review. Value Health. 2015;18(8):1126–37.CrossRefPubMedGoogle Scholar
  25. 25.
    Malinowski KP, Kawalec P. Health utility of patients with Crohn’s disease and ulcerative colitis: a systematic review and meta-analysis. Expert Rev Pharmacoecon Outcomes Res. 2016;16(4):441–53.CrossRefPubMedGoogle Scholar
  26. 26.
    Moayeri F, Hsueh YS, Clarke P, Hua X, Dunt D. Health state utility value in chronic obstructive pulmonary disease (COPD); the challenge of heterogeneity: a systematic review and meta-analysis. COPD. 2016;13(3):380–98.CrossRefPubMedGoogle Scholar
  27. 27.
    Kwon J, Kim SW, Ungar WJ, Tsiplova K, Madan J, Petrou S. A systematic review and meta-analysis of childhood health utilities. Med Decis Making. 2018;38(3):277–305.  https://doi.org/10.1177/0272989X17732990.CrossRefPubMedGoogle Scholar
  28. 28.
    Brown DS, Trogdon JG, Ekwueme DU, Chamiec-Case L, Guy GP Jr, Tangka FK, et al. Health state utility impact of breast cancer in U.S. women aged 18–44 years. Am J Prev Med. 2016;50(2):255–61.CrossRefPubMedGoogle Scholar
  29. 29.
    Karnon J. Heath state utility values for cost-effectiveness models. Pharmacoeconomics. 2017.  https://doi.org/10.1007/s40273-017-0537-x.Google Scholar
  30. 30.
    Papaioannou D, Brazier J, Paisley S. NICE DSU Technical Support Document 9: The identification, review and synthesis of health state utility values from the literature. London: NICE Decision Support Unit Technical Support Documents; 2010.Google Scholar
  31. 31.
    Papaioannou D, Brazier J, Paisley S. Systematic searching and selection of health state utility values from the literature. Value Health. 2013;16(4):686–95.CrossRefPubMedGoogle Scholar
  32. 32.
    Ara R, Brazier J, Peasgood T, Paisley S. The identification, review and synthesis of health state utility values from the literature. Pharmacoeconomics. 2017;35(Suppl 1):43–55.CrossRefPubMedGoogle Scholar
  33. 33.
    Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339:b2535.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    NHS Economic Evaluation Database [database on the Internet]. Centre for Reviews and Dissemination. 2010. http://www.york.ac.uk/inst/crd. Accessed: Dec 2017.
  35. 35.
    CEA Registry [database on the Internet]. Tufts Medical Centre. 2010. Accessed Dec 2017.Google Scholar
  36. 36.
    Higgins JPT. Cochrane handbook for systematic reviews of interventions. www.handbook.cochrane.org: The Cochrane Collaboration; 2011.
  37. 37.
    Parkin D, Devlin N. Is there a case for using visual analogue scale valuations in cost-utility analysis? Health Econ. 2006;15(7):653–64.CrossRefPubMedGoogle Scholar
  38. 38.
    Schulz KF, Altman DG, Moher D, Group C. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010;340:c332.CrossRefGoogle Scholar
  39. 39.
    Vandenbroucke JP, von Elm E, Altman DG, Gotzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the reporting of observational studies in epidemiology (STROBE): explanation and elaboration. Ann Intern Med. 2007;147(8):W163–94.CrossRefPubMedGoogle Scholar
  40. 40.
    Husereau D, Drummond M, Petrou S, Carswell C, Moher D, Greenberg D, et al. Consolidated health economic evaluation reporting standards (CHEERS)-explanation and elaboration: a report of the ISPOR health economic evaluation publication guidelines good reporting practices task force. Value Health. 2013;16(2):231–50.CrossRefPubMedGoogle Scholar
  41. 41.
    Cooper N, Coyle D, Abrams K, Mugford M, Sutton A. Use of evidence in decision models: an appraisal of health technology assessments in the UK since 1997. J Health Serv Res Policy. 2005;10(4):245–50.CrossRefPubMedGoogle Scholar
  42. 42.
    Brazier J, Deverill M. A checklist for judging preference-based measures of health related quality of life: learning from psychometrics. Health Econ. 1999;8(1):41–51.CrossRefPubMedGoogle Scholar
  43. 43.
    Weinstein MC, O’Brien B, Hornberger J, Jackson J, Johannesson M, McCabe C, et al. Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR task force on good research practices-modeling studies. Value Health. 2003;6(1):9–17.CrossRefPubMedGoogle Scholar
  44. 44.
    Cheng AK, Niparko JK. Cost-utility of the cochlear implant in adults: a meta-analysis. Arch Otolaryngol Head Neck Surg. 1999;125(11):1214–8.CrossRefPubMedGoogle Scholar
  45. 45.
    Post PN, Stiggelbout M, Wakker PP. The utility of health states after stroke: a systematic review of the literature. Stroke. 2001;32(6):1425–9.CrossRefPubMedGoogle Scholar
  46. 46.
    Tengs TO, Lin TH. A meta-analysis of quality-of-life estimates for stroke. Pharmacoeconomics. 2003;21(3):191–200.CrossRefPubMedGoogle Scholar
  47. 47.
    Bremner KE, Chong CA, Tomlinson G, Alibhai SM, Krahn MD. A review and meta-analysis of prostate cancer utilities. Med Decis Mak. 2007;27(3):288–98.CrossRefGoogle Scholar
  48. 48.
    Liem YS, Bosch JL, Hunink MG. Preference-based quality of life of patients on renal replacement therapy: a systematic review and meta-analysis. Value Health. 2008;11(4):733–41.CrossRefPubMedGoogle Scholar
  49. 49.
    Peasgood T, Herrmann K, Kanis JA, Brazier JE. An updated systematic review of health state utility values for osteoporosis related conditions. Osteoporos Int. 2009;20(6):853–68.CrossRefPubMedGoogle Scholar
  50. 50.
    Sturza J. A review and meta-analysis of utility values for lung cancer. Med Decis Mak. 2010;30(6):685–93.CrossRefGoogle Scholar
  51. 51.
    Lung TW, Hayes AJ, Hayen A, Farmer A, Clarke PM. A meta-analysis of health state valuations for people with diabetes: explaining the variation across methods and implications for economic evaluation. Qual Life Res. 2011;20(10):1669–78.CrossRefPubMedGoogle Scholar
  52. 52.
    Wyld M, Morton RL, Hayen A, Howard K, Webster AC. A systematic review and meta-analysis of utility-based quality of life in chronic kidney disease treatments. PLoS Med. 2012;9(9):e1001307.CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Si L, Winzenberg TM, de Graaff B, Palmer AJ. A systematic review and meta-analysis of utility-based quality of life for osteoporosis-related conditions. Osteoporos Int. 2014;25(8):1987–97.PubMedGoogle Scholar
  54. 54.
    Tran BX, Nguyen LH, Ohinmaa A, Maher RM, Nong VM, Latkin CA. Longitudinal and cross sectional assessments of health utility in adults with HIV/AIDS: a systematic review and meta-analysis. BMC Health Serv Res. 2015;15:7.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Sampson CJ, Tosh JC, Cheyne CP, Broadbent D, James M. Health state utility values for diabetic retinopathy: protocol for a systematic review and meta-analysis. Syst Rev. 2015;4:15.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Sutton AJ, Abrams KR, Jones DR, Sheldon TA, Song F. Methods for meta-analysis in medical research. Chichester: John Wiley & Sons; 2000.Google Scholar
  57. 57.
    Cochran WG. The comparison of percentages in matched samples. Biometrika. 1950;37(3–4):256–66.CrossRefPubMedGoogle Scholar
  58. 58.
    Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58.CrossRefPubMedGoogle Scholar
  59. 59.
    DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88.CrossRefPubMedGoogle Scholar
  60. 60.
    Ades AE, Lu G, Higgins JP. The interpretation of random-effects meta-analysis in decision models. Med Decis Mak. 2005;25(6):646–54.CrossRefGoogle Scholar
  61. 61.
    Welton NJ, Soares MO, Palmer S, Ades AE, Harrison D, Shankar-Hari M, et al. Accounting for heterogeneity in relative treatment effects for use in cost-effectiveness models and value-of-information analyses. Med Decis Mak. 2015;35(5):608–21.CrossRefGoogle Scholar
  62. 62.
    Kalaian HA, Raudenbush SWA. multivariate mixed linear model for meta-analysis. Psychol Methods. 1996;1(3):227–35.CrossRefGoogle Scholar
  63. 63.
    Bland M. An introduction to medical statistics. 3rd ed. USA: Oxford University Press; 2000.Google Scholar
  64. 64.
    Higgins JP, Thompson SG. Controlling the risk of spurious findings from meta-regression. Stat Med. 2004;23(11):1663–82.CrossRefPubMedGoogle Scholar
  65. 65.
    Goeman JJ, Solari A. Multiple hypothesis testing in genomics. Stat Med. 2014;33(11):1946–78.CrossRefPubMedGoogle Scholar
  66. 66.
    Dias S, Sutton AJ, Welton NJ, Ades AE. Heterogeneity: subgroups, meta-regression, bias and bias-adjustment. London: NICE Decision Support Unit Technical Support Documents; 2012.Google Scholar
  67. 67.
    Han PP, Holbrook TL, Sise MJ, Sack DI, Sise CB, Hoyt DB, et al. Postinjury depression is a serious complication in adolescents after major trauma: injury severity and injury-event factors predict depression and long-term quality of life deficits. J Trauma. 2011;70(4):923–30.CrossRefPubMedGoogle Scholar
  68. 68.
    Braam KI, van Dijk-Lokkart EM, van Dongen JM, van Litsenburg RRL, Takken T, Huisman J, et al. Cost-effectiveness of a combined physical exercise and psychosocial training intervention for children with cancer: Results from the quality of life in motion study. Eur J Cancer Care (Engl). 2017.  https://doi.org/10.1111/ecc.12586.Google Scholar
  69. 69.
    Pullenayegum EM, Tarride JE, Xie F, Goeree R, Gerstein HC, O’Reilly D. Analysis of health utility data when some subjects attain the upper bound of 1: are Tobit and CLAD models appropriate? Value Health. 2010;13(4):487–94.CrossRefPubMedGoogle Scholar
  70. 70.
    Dias S, Welton NJ, Sutton AJ, Ades AE. A generalised linear modelling framework for pairwise and network meta-analysis of randomised controlled trials. London: NICE Decision Support Unit Technical Support Documents; 2014.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Warwick Clinical Trials Unit, Warwick Medical SchoolUniversity of WarwickCoventryUK
  2. 2.School of Health and Related ResearchThe University of SheffieldSheffieldUK

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