, Volume 36, Issue 1, pp 7–15 | Cite as

Oncology Modeling for Fun and Profit! Key Steps for Busy Analysts in Health Technology Assessment

  • Jaclyn Beca
  • Don HusereauEmail author
  • Kelvin K. W. Chan
  • Neil Hawkins
  • Jeffrey S. Hoch
Current Opinion


In evaluating new oncology medicines, two common modeling approaches are state transition (e.g., Markov and semi-Markov) and partitioned survival. Partitioned survival models have become more prominent in oncology health technology assessment processes in recent years. Our experience in conducting and evaluating models for economic evaluation has highlighted many important and practical pitfalls. As there is little guidance available on best practices for those who wish to conduct them, we provide guidance in the form of ‘Key steps for busy analysts,’ who may have very little time and require highly favorable results. Our guidance highlights the continued need for rigorous conduct and transparent reporting of economic evaluations regardless of the modeling approach taken, and the importance of modeling that better reflects reality, which includes better approaches to considering plausibility, estimating relative treatment effects, dealing with post-progression effects, and appropriate characterization of the uncertainty from modeling itself.


Author contributions

JB and JSH provided the original inspiration and key content for this work. DH led the writing of the manuscript including the drafting of the outline and manuscript and is the guarantor of this work. All authors approved the outline of the work, helped to write and revise the manuscript, and read and approved the final version of the manuscript.

Compliance with ethical standards


No funding was received for the preparation of this work. All authors provided in-kind contributions.

Conflict of interest

All authors have signed conflict of interest forms and read information regarding disclosure of potential conflict of interest at and declare the following: accepting consulting fees from medical device and pharmaceutical companies who may have interest in the work (DH, NH); no other relationships or activities that could appear to have influenced the submitted work (JB, KKWC, JSH). All authors declare a significant interest in improving the state of the art of oncology health technology assessment.


  1. 1.
    Mathes T, Jacobs E, Morfeld J-C, Pieper D. Methods of international health technology assessment agencies for economic evaluations: a comparative analysis. BMC Health Serv Res. 2013;13:371.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Tsoi B, Masucci L, Campbell K, Drummond M, O’Reilly D, Goeree R. Harmonization of reimbursement and regulatory approval processes: a systematic review of international experiences. Expert Rev Pharmacoecon Outcomes Res. 2013;13:497–511.CrossRefPubMedGoogle Scholar
  3. 3.
    Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the economic evaluation of health care programmes. 4th ed. Oxford: Oxford University Press; 2015.Google Scholar
  4. 4.
    Caro JJ, Briggs AH, Siebert U, Kuntz KM. ISPOR-SMDM modeling good research practices task force. Modeling good research practices: overview. A report of the ISPOR-SMDM modeling good research practices task force: 1. Value Health. 2012;15:796–803.CrossRefPubMedGoogle Scholar
  5. 5.
    Hoch JS, Beca J, Sabharwal M, Livingstone SW, Fields ALA. Does it matter whether Canada’s separate health technology assessment process for cancer drugs has an economic rationale? Pharmacoeconomics. 2015;33:879–82.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    McCabe C, Paul A, Fell G, Paulden M. Cancer Drugs Fund 2.0: a missed opportunity? Pharmacoeconomics. 2016;34:629–33.CrossRefPubMedGoogle Scholar
  7. 7.
    Grieve R, Hawkins N, Pennington M. Extrapolation of survival data in cost-effectiveness analyses: improving the current state of play. Med Decis Making. 2013;33:740–2.CrossRefPubMedGoogle Scholar
  8. 8.
    Latimer NR. Survival analysis for economic evaluations alongside clinical trials: extrapolation with patient-level data: inconsistencies, limitations, and a practical guide. Med Decis Making. 2013;33:743–54.CrossRefPubMedGoogle Scholar
  9. 9.
    Bagust A, Beale S. Survival analysis and extrapolation modeling of time-to-event clinical trial data for economic evaluation: an alternative approach. Med Decis Making. 2014;34:343–51.CrossRefPubMedGoogle Scholar
  10. 10.
    Jackson C, Stevens J, Ren S, Latimer N, Bojke L, Manca A, et al. Extrapolating survival from randomized trials using external data: a review of methods. Med Decis Making. 2017;37(4):377–90.CrossRefPubMedGoogle Scholar
  11. 11.
    Woods B, Sideriis E, Palmer S, Latimer N, Soares M. Nice DSU technical support document 19: partitioned survival analysis for decision modelling in health care. A critical review. Decision Support Unit, ScHARR, University of Sheffield; 2017. Accessed 3 Nov 2017.
  12. 12.
    Glasziou PP, Simes RJ, Gelber RD. Quality adjusted survival analysis. Stat Med. 1990;9:1259–76.CrossRefPubMedGoogle Scholar
  13. 13.
    Glasziou PP, Cole BF, Gelber RD, Hilden J, Simes RJ. Quality adjusted survival analysis with repeated quality of life measures. Stat Med. 1998;17:1215–29.CrossRefPubMedGoogle Scholar
  14. 14.
    Gelber RD, Gelman RS, Goldhirsch A. A quality-of-life-oriented endpoint for comparing therapies. Biometrics. 1989;45:781–95.CrossRefPubMedGoogle Scholar
  15. 15.
    Feldstein ML. Quality-of-life-adjusted survival for comparing cancer treatments: a commentary on TWiST and Q-TWiST. Cancer. 1991;67:851–4.CrossRefPubMedGoogle Scholar
  16. 16.
    Woods B. Partitioned survival analysis: a critical review of the approach and application to decision modelling in health care. Smdm; 2016. Available from: Accessed 9 Aug 2016.
  17. 17.
    Masucci L, Beca J, Sabharwal M, Hoch JS. Methodological issues in economic evaluations submitted to the pan-Canadian Oncology Drug Review (pCODR). Pharmacoeconomics Open. 2017;1–9.Google Scholar
  18. 18.
    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;16:231–50.CrossRefPubMedGoogle Scholar
  19. 19.
    Vemer P, Corro Ramos I, van Voorn GAK, Al MJ, Feenstra TL. AdViSHE: a validation-assessment tool of health-economic models for decision makers and model users. Pharmacoeconomics. 2016;34:349–61.CrossRefPubMedGoogle Scholar
  20. 20.
    Minacori R, Bonastre J, Lueza B, Marguet S, Levy P. How to model survival in cost-effectiveness analysis? Differences between Markov and partitioned survival analysis models. Value Health. 2015;18:A704.CrossRefPubMedGoogle Scholar
  21. 21.
    Hettle R, Posnett J, Borrill J. Challenges in economic modeling of anticancer therapies: an example of modeling the survival benefit of olaparib maintenance therapy for patients with BRCA-mutated platinum-sensitive relapsed ovarian cancer. J Med Econ. 2015;18:516–24.CrossRefPubMedGoogle Scholar
  22. 22.
    NICE. Ofatumumab in combination with chlorambucil or bendamustine for untreated chronic lymphocytic leukaemia: the company’s submission. Guidance and guidelines. Available from: Accessed 4 Nov 2016.
  23. 23.
    NICE. Enzalutamide for metastatic hormone-relapsed prostate cancer previously treated with a docetaxel-containing regimen: the manufacturer’s submission. Guidance and guidelines. Available from: Accessed 4 Nov 2016.
  24. 24.
    Goodacre S, McCabe C. Being economical with the truth: how to make your idea appear cost effective. EMJ. 2002;19:301–4.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Latimer NR. Survival analysis for economic evaluations alongside clinical trials: extrapolation with patient-level data. London: National Institute for Health and Care Excellence (NICE); 2013. Available from: Accessed 29 Mar 2017.
  26. 26.
    Veroniki AA, Straus SE, Soobiah C, Elliott MJ, Tricco AC. A scoping review of indirect comparison methods and applications using individual patient data. BMC Med Res Methodol. 2016;16:47.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Hoaglin DC, Hawkins N, Jansen JP, Scott DA, Itzler R, Cappelleri JC, et al. Conducting indirect-treatment-comparison and network-meta-analysis studies: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices. Part 2. Value Health. 2011;14:429–37.CrossRefPubMedGoogle Scholar
  28. 28.
    Hutton B, Salanti G, Caldwell DM, Chaimani A, Schmid CH, Cameron C, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med. 2015;162:777–84.CrossRefPubMedGoogle Scholar
  29. 29.
    Jönsson L, Sandin R, Ekman M, Ramsberg J, Charbonneau C, Huang X, et al. Analyzing overall survival in randomized controlled trials with crossover and implications for economic evaluation. Value Health. 2014;17:707–13.CrossRefPubMedGoogle Scholar
  30. 30.
    Guyot P, Welton NJ, Ouwens MJNM, Ades AE. Survival time outcomes in randomized, controlled trials and meta-analyses: the parallel universes of efficacy and cost-effectiveness. Value Health. 2011;14:640–6.CrossRefPubMedGoogle Scholar
  31. 31.
    Williams C, Lewsey JD, Briggs AH, Mackay DF. Cost-effectiveness analysis in R using a multi-state modeling survival analysis framework: a tutorial. Med Decis Making. 2017;37:340.CrossRefPubMedGoogle Scholar
  32. 32.
    Guyot P, Ades AE, Ouwens MJNM, Welton NJ. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Med Res. Methodol. 2012;12:9.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Guyot P, Welton NJ, Ouwens MJNM, Ades AE. Survival time outcomes in randomized, controlled trials and meta-analyses: the parallel universes of efficacy and cost-effectiveness. Value Health. 2011;14:640–6.CrossRefPubMedGoogle Scholar
  34. 34.
    CADTH. Guidelines for the economic evaluation of health technologies: Canada. 4th ed. Available from: Accessed 25 May 2017.
  35. 35.
    García-Albéniz X, Maurel J, Hernán MA. Why post-progression survival and post-relapse survival are not appropriate measures of efficacy in cancer randomized clinical trials. Int J Cancer. 2015;136:2444–7.CrossRefPubMedGoogle Scholar
  36. 36.
    Williams C, Lewsey JD, Mackay DF, Briggs AH. Estimation of survival probabilities for use in cost-effectiveness analyses: a comparison of a multi-state modeling survival analysis approach with partitioned survival and Markov decision-analytic modeling. Med Decis Making. 2017;37(4):427–39.CrossRefPubMedGoogle Scholar
  37. 37.
    Taichman DB, Sahni P, Pinborg A, Peiperl L, Laine C, James A, et al. Data sharing statements for clinical trials: a requirement of the International Committee of Medical Journal Editors. N Engl J Med. 2017;376:2277–9.CrossRefPubMedGoogle Scholar
  38. 38.
    Royston P, Parmar MKB. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med. 2002;21:2175–97.CrossRefPubMedGoogle Scholar
  39. 39.
    Jackson CH, Sharples LD, Thompson SG. Survival models in health economic evaluations: balancing fit and parsimony to improve prediction. Int J Biostat. 2010;6(1) (Article 34).Google Scholar
  40. 40.
    Goeree R, Villeneuve J, Goeree J, Penrod JR, Orsini L, Tahami Monfared AA. Economic evaluation of nivolumab for the treatment of second-line advanced squamous NSCLC in Canada: a comparison of modeling approaches to estimate and extrapolate survival outcomes. J Med Econ. 2016;19:630–44.CrossRefPubMedGoogle Scholar
  41. 41.
    Briggs A, Baker TM, Gilloteau I, Orsini L, Wagner S, Paly V. Partitioned survival versus state transition modeling in oncology: a case study with nivolumab in advanced melanoma. Value Health. 2015;18:A338.CrossRefPubMedGoogle Scholar
  42. 42.
    Beca J. Method matters: partitioned survival models characterize and extrapolate risks differently from Markov models. Smdm; 2016. Available from: Accessed 27 Apr 2017.
  43. 43.
    Krishnamurti TN, Kishtawal CM, LaRow TE, Bachiochi DR, Zhang Z, Williford CE, et al. Improved weather and seasonal climate forecasts from multimodel superensemble. Science. 1999;285:1548–50.CrossRefPubMedGoogle Scholar
  44. 44.
    Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins M, et al. Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature. 2004;430:768–72.CrossRefPubMedGoogle Scholar
  45. 45.
    Afzali HHA, Karnon J. Exploring structural uncertainty in model-based economic evaluations. Pharmacoeconomics. 2015;33:435–43.CrossRefPubMedGoogle Scholar
  46. 46.
    Coyle D, Coyle K. The inherent bias from using partitioned survival models in economic evaluation. Value Health. 2014;17:A194.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Pharmacoeconomics Research UnitCancer Care OntarioTorontoCanada
  2. 2.Institute of Health EconomicsEdmontonCanada
  3. 3.School of Epidemiology and Public HealthUniversity of OttawaOttawaCanada
  4. 4.Sunnybrook Odette Cancer CentreUniversity of TorontoTorontoCanada
  5. 5.Canadian Centre for Applied Research in Cancer ControlTorontoCanada
  6. 6.The University of GlasgowGlasgowUK
  7. 7.The University of California, DavisDavisUSA

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