Advertisement

PharmacoEconomics

, Volume 35, Issue 8, pp 767–776 | Cite as

How to Appropriately Extrapolate Costs and Utilities in Cost-Effectiveness Analysis

  • Laura BojkeEmail author
  • Andrea Manca
  • Miqdad Asaria
  • Ronan Mahon
  • Shijie Ren
  • Stephen Palmer
Practical Application

Abstract

Costs and utilities are key inputs into any cost-effectiveness analysis. Their estimates are typically derived from individual patient-level data collected as part of clinical studies the follow-up duration of which is often too short to allow a robust quantification of the likely costs and benefits a technology will yield over the patient’s entire lifetime. In the absence of long-term data, some form of temporal extrapolation—to project short-term evidence over a longer time horizon—is required. Temporal extrapolation inevitably involves assumptions regarding the behaviour of the quantities of interest beyond the time horizon supported by the clinical evidence. Unfortunately, the implications for decisions made on the basis of evidence derived following this practice and the degree of uncertainty surrounding the validity of any assumptions made are often not fully appreciated. The issue is compounded by the absence of methodological guidance concerning the extrapolation of non-time-to-event outcomes such as costs and utilities. This paper considers current approaches to predict long-term costs and utilities, highlights some of the challenges with the existing methods, and provides recommendations for future applications. It finds that, typically, economic evaluation models employ a simplistic approach to temporal extrapolation of costs and utilities. For instance, their parameters (e.g. mean) are typically assumed to be homogeneous with respect to both time and patients’ characteristics. Furthermore, costs and utilities have often been modelled to follow the dynamics of the associated time-to-event outcomes. However, cost and utility estimates may be more nuanced, and it is important to ensure extrapolation is carried out appropriately for these parameters.

Keywords

Individual Patient Data Health Assessment Questionnaire Score Psoriasis Area Severity Index Decision Uncertainty Baseline Utility 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Laura Bojke and Andrea Manca were primarily responsible for drafting the manuscript. Stephen Palmer, Miqdad Asaria, Ronan Mahon and Shijie Ren contributed towards writing and commented on various versions of the manuscript.

Compliance with Ethical Standards

Funding

Work contributing to this manuscript was conducted as part of a Medical Research Council (MRC) grant “Methods of extrapolating RCT evidence for economic evaluation”, although this manuscript was not prepared during the time of this grant. Laura Bojke was supported in the preparation/submission of this paper by the Health Economics and Outcome Measurement (HEOM) Theme of the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care Yorkshire and Humber (NIHR CLAHRC YH; www.clahrc-yh.nir.ac.uk). The views and opinions expressed are those of the authors, and not necessarily those of the UK National Health Service (NHS), the NIHR or the Department of Health.

Conflicts of interest

Laura Bojke, Andrea Manca, Miqdad Asaria, Ronan Mahan, Stephen Palmer and Shijie Ren all have no conflicts of interest.

References

  1. 1.
    Stafinski T, Menon D, Philippon DJ, et al. Health technology funding decision-making processes around the world. Pharmacoeconomics. 2011;29(6):475–95.CrossRefPubMedGoogle Scholar
  2. 2.
    Byford S, Raftery J. Economics notes. Perspectives in economic evaluation. BMJ. 1998;316(7143):1529–30.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Kind P, Hardman G, Macran S. UK population norms for EQ-5D: discussion paper 172. York: Centre for Health Economics, University of York; 1999.Google Scholar
  4. 4.
    National Institute for Health and Care Excellence. Guide to the methods of technology appraisal. London: NICE; 2013.Google Scholar
  5. 5.
    Salas-Vega S, Bertling A, Mossialos E. A comparative study of drug listing recommendations and the decision-making process in Australia, the Netherlands, Sweden, and the UK. Health Policy. 2016;120(10):1104–14.CrossRefPubMedGoogle Scholar
  6. 6.
    Conrad C. International Society for Pharmacoeconomics and Outcomes Research (ISPOR). The German IQWiG—it’s not NICE benefit assessment in Germany-new sense or nuisance? ISPOR Connections, 2006;12(5). http://www.ispor.org/news/articles/oct06/german_policy.asp. Accessed 23 April 2017.
  7. 7.
    Sculpher MJ, Claxton K, Drummond M, et al. Whither trial-based economic evaluation for health care decision making? Health Econ. 2006;15(7):677–87.CrossRefPubMedGoogle Scholar
  8. 8.
    Collett D. Modelling survival data in medical research. 2nd ed. Boca Raton: Chapman and Hall/CRC; 2003.Google Scholar
  9. 9.
    Latimer N. NICE DSU technical support document 14: survival analysis for economic evaluations alongside clinical trials—extrapolation with patient-level data report by the Decision Support Unit. June 2011 (last updated March 2013). http://www.nicedsu.org.uk/NICE%20DSU%20TSD%20Survival%20analysis.updated%20March%202013.v2.pdf. Accessed 23 April 2017.
  10. 10.
    Claxton K. The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. J Health Econ. 1999;18(3):341–64.CrossRefPubMedGoogle Scholar
  11. 11.
    Drummond MF, Sculpher MJ, Torrance GW, et al. Methods for the economic evaluation of health care programmes. 3rd ed. Oxford: Oxford University Press; 2005.Google Scholar
  12. 12.
    Garside R, Green C, Hoyle M, et al. The effectiveness and cost effectiveness of natalizumab for multiple sclerosis: an evidence review of the submission from Biogen. 2007. https://www.nice.org.uk/guidance/ta127/resources/multiple-sclerosis-natalizumabevaluation-report-evidence-review-group-report2. Accessed 2 May 2017.
  13. 13.
    Garside R, Pitt M, Somerville M, et al. Surveillance of Barrett’s oesophagus: exploring the uncertainty through systematic review, expert workshop and economic modelling. Health Technol Assess 2006;10(8):1–142, iii–iv.Google Scholar
  14. 14.
    Wilson J, Connock M, Song F, et al. Imatinib for the treatment of patients with unresectable and/or metastatic gastrointestinal stromal tumours: systematic review and economic evaluation. Health Technol Assess. 2005;9(25):1–142.CrossRefPubMedGoogle Scholar
  15. 15.
    Bond M, Mealing S, Anderson R, et al. The effectiveness and cost-effectiveness of cochlear implants for severe to profound deafness in children and adults: a systematic review and economic model. Health Technol Assess. 2009;13(44):1–330.CrossRefGoogle Scholar
  16. 16.
    Gospodarevskaya E, Picot J, Cooper K, et al. Ustekinumab for the treatment of moderate to severe psoriasis. Health Technol Assess. 2009;13(Suppl 3):61–6.CrossRefPubMedGoogle Scholar
  17. 17.
    Glazener C, Breeman S, Elders A, et al. Clinical effectiveness and cost-effectiveness of surgical options for the management of anterior and/or posterior vaginal wall prolapse: two randomised controlled trials within a comprehensive cohort study—results from the PROSPECT study. Health Technol Assess. 2016;20(95):1–452.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Madan J, Ades T, Barton P, et al. Consensus decision models for biologics in rheumatoid and psoriatic arthritis: recommendations of a multidisciplinary working party. Rheumatol Ther. 2015;2(2):113–25.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Briggs A, Sculpher M. An introduction to Markov modelling for economic evaluation. Pharmacoeconomics. 1998;13(4):397–409.CrossRefPubMedGoogle Scholar
  20. 20.
    Davis S, Stevenson M, Tappenden P, et al. NICE DSU Technical support document 15: cost-effectiveness modelling using pateint-level simulation. Report by the Decision Support Unit. Sheffield: School of Health and Related Research, University of Sheffield; 2014.Google Scholar
  21. 21.
    Peasgood T, Brazier J. Is meta-analysis for utility values appropriate given the potential impact different elicitation methods have on values? Pharmacoeconomics. 2015;33(11):1101–5.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Peasgood T, Herrmann K, Kanis JA, et al. An updated systematic review of Health State Utility Values for osteoporosis related conditions. Osteoporos Int. 2009;20(6):853–68.CrossRefPubMedGoogle Scholar
  23. 23.
    Wyld M, Morton RL, Hayen A, et al. 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
  24. 24.
    Bravo Vergel Y, Palmer S, Erhorn S, et al. Adalimumab for the treatment of moderate to severe psoriatic arthritis. Southampton: Health Technol Assess; 2007.Google Scholar
  25. 25.
    Loveman E, Jones J, Hartwell D, et al. The clinical effectiveness and cost-effectiveness of topotecan for small cell lung cancer: a systematic review and economic evaluation. Health Technol Assess. 2010;14(19):1–204.CrossRefGoogle Scholar
  26. 26.
    Turner D, Picot J, Cooper K, et al. Adalimumab for the treatment of psoriasis. Health Technol Assess. 2009;13(Suppl. 2):49–54.PubMedGoogle Scholar
  27. 27.
    Paulden M, Rodgers M, Griffin S, et al. Alitretinoin for the treatment of severe chronic hand eczema. Health Technol Assess. 2010;14(Suppl 1):39–46.CrossRefPubMedGoogle Scholar
  28. 28.
    Maund E, McKenna C, Sarowar M, et al. Dronedarone for the treatment of atrial fibrillation and atrial flutter. Health Technol Assess 2010;14(Suppl 2):55–62.Google Scholar
  29. 29.
    Rodgers M, Epstein D, Bojke L, et al. Etanercept, infliximab and adalimumab for the treatment of psoriatic arthritis: a systematic review and economic evaluation. Health Technol Assess 2011;15(10):i–xxi, 1–329.Google Scholar
  30. 30.
    Hatswell AJ, Pennington B, Pericleous L, et al. Patient-reported utilities in advanced or metastatic melanoma, including analysis of utilities by time to death. Health Qual Life Outcome. 2014;12:140.CrossRefGoogle Scholar
  31. 31.
    Kelly E, Stoye G, Vera-Hernandez M. Public hospital spending in England: evidence from National Health Service administrative records. Fiscal Stud. 2016;37(3–4):433–59.CrossRefGoogle Scholar
  32. 32.
    Asaria M, Doran T, Cookson R. The costs of inequality: whole-population modelling study of lifetime inpatient hospital costs in the English National Health Service by level of neighbourhood deprivation. J Epidemiol Commun Health. 2016;70(10):990–6.CrossRefGoogle Scholar
  33. 33.
    Epstein DM, Sculpher MJ, Clayton TC, et al. Costs of an early intervention versus a conservative strategy in acute coronary syndrome. Int J Cardiol. 2008;127(2):240–6.CrossRefPubMedGoogle Scholar
  34. 34.
    Alva M, Gray A, Mihaylova B, et al. The effect of diabetes complications on health-related quality of life: the importance of longitudinal data to address patient heterogeneity. Health Econ. 2014;23(4):487–500.CrossRefPubMedGoogle Scholar
  35. 35.
    Woolacott N, Bravo Vergel Y, Hawkins N, et al. Etanercept and infliximab for the treatment of psoriatic arthritis: a systematic review and economic evaluation. Health Technol Assess 2006;10(31):iii–iv, xiii–xvi, 1–239.Google Scholar
  36. 36.
    Karnon J, Czoski-Murray C, Smith K, et al. A preliminary model-based assessment of the cost-utility of a screening programme for early age-related macular degeneration. Health Technol Assess 2008;12(27):iii–iv, ix–124.Google Scholar
  37. 37.
    Corbett M, Bojke L, Chehadah F, et al. Certolizumab pegol and secukinumab for treating active psoriatic arthritis following inadequate response to disease modifying anti-rheumatic drugs. PROSPERO 2016:CRD42016033357. http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42016033357. Accessed 26 Apr 2017.
  38. 38.
    National Emphysema Treatment Trial Research Group. Cost effectiveness of lung-volume–reduction surgery for patients with severe emphysema. N Engl J Med. 2003;348:2092–102.CrossRefGoogle Scholar
  39. 39.
    Grant A, Wileman S, Ramsay C, et al. REFLUX Trial Group. The effectiveness and cost-effectiveness of minimal access surgery amongst people with gastro-oesophageal reflux disease—a UK collaborative study. The REFLUX Trial. Health Technol Assess. 2008;12(31):1–181.CrossRefPubMedGoogle Scholar
  40. 40.
    McKenna C, McDaid C, Suekarran S, et al. Enhanced external counterpulsation for the treatment of stable angina and heart failure: a systematic review and economic analysis. Health Technol Assess 2009; 13(24): iii–iv, ix–xi, 1–90.Google Scholar
  41. 41.
    de Vries EF, Rabelink TJ, van den Hout WB. Modelling the cost-effectiveness of delaying end-stage renal disease. Nephron. 2016;133(2):89–97.CrossRefPubMedGoogle Scholar
  42. 42.
    Keating CL, Dixon JB, Moodie ML, et al. Cost-effectiveness of surgically induced weight loss for the management of type 2 diabetes: modeled lifetime analysis. Diabetes Care. 2009;32(4):567–74.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Harrison DA, Prabhu G, Grieve R, et al. Risk Adjustment In Neurocritical care (RAIN)—prospective validation of risk prediction models for adult patients with acute traumatic brain injury to use to evaluate the optimum location and comparative costs of neurocritical care: a cohort study. Health Technol Assess 2013;17(23):vii–viii, 1–350.Google Scholar
  44. 44.
    Stevenson M, Lloyd-Jones M, Papaioannou D. Vitamin K to prevent fractures in older women: a systematic review and economic evaluation. Health Technol Assess. 2009;13(45):iii–xi, 1–134.Google Scholar
  45. 45.
    Henriksson M, Epstein DM, Palmer SJ, et al. The cost-effectiveness of an early interventional strategy in non-ST-elevation acute coronary syndrome based on the RITA 3 trial. Heart. 2008;94(6):717–23.CrossRefPubMedGoogle Scholar
  46. 46.
    Main C, Bojke L, Griffin S, et al. Topotecan, pegylated liposomal doxorubicin hydrochloride and paclitaxel for second-line or subsequent treatment of advanced ovarian cancer: a systematic review and economic evaluation. Health Technol Assess. 2006;10(9):1–132.CrossRefGoogle Scholar
  47. 47.
    Batty A, Winn B, Pericleous L, et al. A comparison of general population and patient utility values for advanced melanoma. European Society for Medical Oncology. Value Health. 2012;15:A277–A575.Google Scholar
  48. 48.
    Kind P, Hardman G, Macran S. UK population norms for EQ-5D. York Centre for Health Economics discussion paper. York: University of York; 1999. p. 172.Google Scholar
  49. 49.
    Peek GJ, Clemens F, Elbourne D, et al. CESAR: conventional ventilatory support vs extracorporeal membrane oxygenation for severe adult respiratory failure. BMC Health Serv Res. 2006;6:163.Google Scholar
  50. 50.
    Jackson CH, Thompson SG, Sharples LD. Accounting for uncertainty in health economic decision models by using model averaging. J R Stat Soc Ser A Stat Soc. 2009;172(2):383–404.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Ara R, Wailoo A. NICE DSU technical support document 12: the use of health state utility values in decision models. Sheffield: School of Health and Related Research, University of Sheffield; 2011.Google Scholar
  52. 52.
    van Duin MJ, Conde R, Wijnen B, et al. The impact of comorbidities on costs, utilities and health-related quality of life among HIV patients in a clinical setting in Bogotá. Epub: Expert Rev Pharmacoecon Outcome Res; 2016.Google Scholar
  53. 53.
    Briggs AH, Parfrey PS, Khan N, et al. Analyzing health-related quality of life in the EVOLVE trial: the joint impact of treatment and clinical events. Med Decis Making. 2016;36(8):965–72.CrossRefPubMedGoogle Scholar
  54. 54.
    Oostenbrink JB, Al MJ, Rutten-van Mölken MP. Methods to analyse cost data of patients who withdraw in a clinical trial setting. Pharmacoeconomics. 2003;21(15):1103–12.CrossRefPubMedGoogle Scholar
  55. 55.
    Rappange DR, van Baal PH, van Exel NJ, et al. Unrelated medical costs in life-years gained: should they be included in economic evaluations of healthcare interventions? Pharmacoeconomics. 2008;26(10):815–30.CrossRefPubMedGoogle Scholar
  56. 56.
    Morton A, Adler AI, Bell D, et al. Unrelated future costs and unrelated future benefits: reflections on NICE guide to the methods of technology appraisal. Health Econ. 2016;25(8):933–8.CrossRefPubMedGoogle Scholar
  57. 57.
    van Baal P, Meltzer D, Brouwer W. Future costs, fixed healthcare budgets, and the decision rules of cost-effectiveness analysis. Health Econ. 2016;25(2):237–48.CrossRefPubMedGoogle Scholar
  58. 58.
    Claxton K, Palmer S, Longworth L, et al. Uncertainty, evidence and irrecoverable costs: informing approval, pricing and research decisions for health technologies. CHE discussion paper 69. York: Centre for Health Economics; 2011.Google Scholar
  59. 59.
    Griffin S, Claxton KP, Palmer SJ, et al. Dangerous omissions: the consequences of ignoring decision uncertainty. Health Econ. 2011;20(2):212–24.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Centre for Health EconomicsUniversity of YorkYorkUK
  2. 2.University of SheffieldSheffieldUK

Personalised recommendations