Oncology from an HTA and Health Economic Perspective

  • Clement Francois
  • Junwen Zhou
  • Michał Pochopien
  • Leila Achour
  • Mondher ToumiEmail author
Part of the Recent Results in Cancer Research book series (RECENTCANCER, volume 213)


In this chapter, we will present and discuss the challenges of assessing oncology products from a health economic perspective. We will provide a brief introduction on the need for economic evaluation in health care and focus on cost-effectiveness and comparative aspects of the evaluation of oncology products, which are of paramount interest to HTA decision-making bodies using economic evaluation in their decision-making framework. As the burden of oncology is well-documented, we do not discuss it in detail here. Before we address the specific issue of oncology, we will briefly define the critical aspects of HTA assessment and also define what a cost-effectiveness analysis is and why economic modelling is the most appropriate tool to assess the cost-effectiveness of oncology products. We will touch upon the prices of oncology drugs and the questions that high prices raise regarding funding and availability. We then present an overview of the general structure of an oncology cost-effectiveness model. Usually, this is quite simple, representing response, progression, advanced-stage disease and death. Despite the relative simplicity of these models, some issues may render the evaluation more complex; we will touch upon these in this chapter:
  • Issue with clinical inputs due to the design of randomised clinical trials (e.g. cross-over designs involving a treatment switch)

  • Need for survival extrapolation and limitations of current parametric models

  • Rare conditions with limited economic and comparative evidence available

  • High pace of clinical development

Finally, we will conclude with a discussion of the uncertainty around the evaluation of oncology products and the major evolution expected in health economics in oncology.


Health technology assessment Cost-effectiveness Oncology drugs Decision-making 


  1. Annemans L (2018) Extrapolation in oncology modelling: novel methods for novel compounds. Available from: Accessed on 09 Feb 2018
  2. Bai Y, Xu Y, Wu B (2017) Cost-effectiveness and budget impact analysis of apatinib for advanced metastatic gastric cancer from the perspective of health insurance system. Gastroenterol Res Pract 2017:2816737CrossRefGoogle Scholar
  3. Califf RM, Zarin DA, Kramer JM et al (2012) Characteristics of clinical trials registered in, 2007–2010. Jama 307:1838–1847CrossRefGoogle Scholar
  4. Cohen D (2017) Most drugs paid for by £1.27bn cancer drugs fund had no “meaningful benefit”. BMJ 357:j2097.
  5. Collins M, Latimer N (2013) NICE’s end of life criteria: who gains, who loses? BMJ 346:f1363CrossRefGoogle Scholar
  6. Crabb N, Stevens A (2016) Exploring the assessment and appraisal of regenerative medicines and cell therapy products. NICEGoogle Scholar
  7. Cressman S, Browman GP, Hoch JS et al (2015) A time-trend economic analysis of cancer drug trials. Oncologist 20:729–736CrossRefGoogle Scholar
  8. de Souza JA, Yap BJ, Hlubocky FJ et al (2014) The development of a financial toxicity patient-reported outcome in cancer: the COST measure. Cancer 120(20):3245–3253CrossRefGoogle Scholar
  9. Dentaland Pharmaceutical Benefits Agency (TLV) (2012) Reimbursement decision for afinitor. TLV, Stockholm, SwedenGoogle Scholar
  10. Experts in Chronic Myeloid Leukemia (2013) The price of drugs for chronic myeloid leukaemia (CML) is a reflection of the unsustainable prices of cancer drugs: from the perspective of a large group of CML experts. Blood 121(22):4439–4442CrossRefGoogle Scholar
  11. Fojo T, Grady C (2009) How much is life worth: cetuximab, non-small cell lung cancer, and the $440 billion question. J Natl Cancer Inst 101(15):1044–1048CrossRefGoogle Scholar
  12. Gibson E, Koblbauer I, Begum N et al (2017) Modelling the survival outcomes of immuno-oncology drugs in economic evaluations: a systematic approach to data analysis and extrapolation. PharmacoEconomics 35:1257–1270CrossRefGoogle Scholar
  13. Gold MR, Siegel JE, Russell LB, Weinstein MC (1996) Cost-effectiveness in health and medicine. Oxford University Press, New YorkGoogle Scholar
  14. Isbary G, Staab TR, Amelung VE, Dintsios C-M, Iking-Konert C, Mariotti Nesurini S, Walter M, Ruof J (2017) The effect of crossover in oncology clinical trials on evidence levels in early benefit assessment in Germany. Value HealthGoogle Scholar
  15. Jönsson L, Sandin R, Ekman M, Ransberg J et al (2014) Analyzing overall survival in randomized controlled trials with crossover and implications for economic evaluation. Value Health 17:707–713CrossRefGoogle Scholar
  16. Kumar R (2013) Health economics and cost-effectiveness research with special reference to hemato-oncology. Med J Armed Forces India 69(3):273–277CrossRefGoogle Scholar
  17. Latimer N (2011) NICE DSU technical support document 14: undertaking survival analysis for economic evaluations alongside clinical trials—extrapolation with patient-level data. Available from Accessed on 09 Feb 2018
  18. Latimer NR (2013) Survival analysis for economic evaluations alongside clinical trials—extrapolation with patient-level data: inconsistencies, limitations, and a practical guide. Med Decis Making 33:743–754CrossRefGoogle Scholar
  19. Latimer NR, Abrams KR (2014) NICE DSU technical support document 16: adjusting survival time estimates in the presence of treatment switching. Available from Accessed on 09 Feb 2018
  20. Marsh K, Xu P, Orfanos P, Gordon J, Griebsch I (2014) Model-based cost-effectiveness analyses for the treatment of chronic lymphocytic leukaemia: a review of methods to model disease outcomes and estimate utility. Pharmacoeconomics 32(10):981–993CrossRefGoogle Scholar
  21. Miller JD, Foley KA, Russell MW (2014) Current challenges in health economic modelling of cancer therapies: a research inquiry. Am Health Drug Benefits 7:153–162PubMedPubMedCentralGoogle Scholar
  22. Murray CJ, Evans DB, Acharya A, Baltussen RM (2000) Development of WHO guidelines on generalized cost-effectiveness analysis. Health Econ 9:235–251CrossRefGoogle Scholar
  23. National Institute for Health and Care Excellence (2013) Guide to the methods of technology appraisal. NICE, LondonGoogle Scholar
  24. National Institute of Health and Care Excellence (2011) Everolimus for the second-line treatment of advanced renal cell carcinoma (NICE technology appraisal guidance 219). NICE, London, UKGoogle Scholar
  25. Neumann PJ, Bliss SK, Chambers JD (2012) Therapies for advanced cancers pose a special challenge for health technology assessment organizations in many countries. Health Aff (Millwood) 31:700–708CrossRefGoogle Scholar
  26. “Off-Label” indications for oncology drug use and drug compendia: history and current status. J Oncol Pract 1:102–105 (2005)Google Scholar
  27. Oncology Health Economic Modeling Post Progression Working Group of the ISPOR Oncology SIG. ISPOR, Boston (2017)Google Scholar
  28. Prasad V, Mailankody S (2016) The UK cancer drugs fund experiment and the US cancer drug cost problem: bearing the cost of cancer drugs until it is unbearable. Mayo Clin Proc 91(6):707–712CrossRefGoogle Scholar
  29. Ryder HF, McDonough CF, Tosteson A, Lurie JD (2009) Decision analysis and cost-effectiveness analysis. Semin Spine Surg 21(4):216–222CrossRefGoogle Scholar
  30. Sarin R (2008) Criteria for deciding cost-effectiveness for expensive new anti-cancer agents. J Cancer Res Ther 4:1–2CrossRefGoogle Scholar
  31. Sonnenberg FA, Beck JR (1993) Markov models in medical decision making: a practical guide. Med Decis Making 13(4):322–338CrossRefGoogle Scholar
  32. Toumi M (2017) Introduction to market access for pharmaceuticals. CRC PressGoogle Scholar
  33. Watkins C, Huang X, Latimer N, Tang Y, Wright EJ (2013) Adjusting overall survival for treatment switches: commonly used methods and practical application. Pharm Stat 12:348–357CrossRefGoogle Scholar
  34. Weinstein MC, O’Brien B, Hornberger J, Jackson J, Johannesson M, McCabe C, Luce BR (2003) 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 6(1):9–17CrossRefGoogle Scholar
  35. Zafar SY, Peppercorn JM, Schrag D et al (2013) The financial toxicity of cancer treatment: a pilot study assessing out-of-pocket expenses and the insured cancer patient’s experience. Oncologist 13(4):381–390CrossRefGoogle Scholar
  36. Ziomek J, El Mouaddin N, Ng T et al (2017) Analysis of recent approvals of immuno-oncology drugs across England, Scotland, Germany and France. Value Health A399–A811Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Clement Francois
    • 1
  • Junwen Zhou
    • 1
  • Michał Pochopien
    • 1
  • Leila Achour
    • 2
  • Mondher Toumi
    • 1
    Email author
  1. 1.Public Health Department, Research Unit EA 3279Aix-Marseille UniversityMarseilleFrance
  2. 2.University Paris DauphineParisFrance

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