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Recent Developments in Health Economic Modelling of Cancer Therapies

  • William GreenEmail author
  • Matthew Taylor
Chapter
Part of the Recent Results in Cancer Research book series (RECENTCANCER, volume 213)

Abstract

Arguably, the most common structure currently adopted for oncology modelling is the three-state partitioned survival model with the following states: stable disease, post-progression and dead. This design can, therefore, be adopted to capture the progressive nature of cancer. This chapter outlines the three-state model approach as well as introducing several other key aspects of economic modelling in oncology.

Keywords

Health economic modelling Survival model Quality-adjusted life year Utilities 

References

  1. Bradbury MJ, Clark TG, Love SB et al (2003) Survival analysis part III: multivariate data analysis—choosing a model and assessing its adequacy and fit. Br J Cancer 89(4):605–611CrossRefGoogle Scholar
  2. Clark TG, Bradburn MJ, Love SB et al (2003) Survival analysis part I: basic concepts and first analyses. Br J Cancer 89(2):232–238CrossRefGoogle Scholar
  3. Davis S (2014) Assessing technologies that are not cost-effective at a zero price. Report by the NICE Decision Support Unit. University of SheffieldGoogle Scholar
  4. Guyot P, Welton NJ, Ouwens MJ et al (2011) Survival time outcomes in randomized, controlled trials and meta-analyses: the parallel universes of efficacy and cost-effectiveness. Value Health 14(5):640–646CrossRefGoogle Scholar
  5. Guyot P, Ades AE, Ouwens MJ et al (2012) Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Med Res Methodol 12:9CrossRefGoogle Scholar
  6. Guyot P, Ades AE, Beasley M (2017) Extrapolation of survival curves from cancer trials using external information. Med Decis Making 35:353–366CrossRefGoogle Scholar
  7. Hoyle MW, Henley W (2011) Improved curve fits to summary survival data: application to economic evaluation of health technologies. BMC Med Res Methodol 11:139CrossRefGoogle Scholar
  8. Ishak KJ, Proskorovsky I, Benedict A (2015) Simulation and matching-based approaches for indirect comparison of treatments. Pharmacoeconomics 33(6):537–549CrossRefGoogle Scholar
  9. Latimer N (2013). Nice DSU Technical Support Document. Survival analysis for economic evaluations alongside clinical trials–extrapolation with patient-level dataGoogle Scholar
  10. Latimer NR, Abrams KR, Lambert PC et al (2017) Adjusting for treatment switching in randomised controlled trials—a simulation study and a simplified two-stage method. Stat Methods Med Res 26:724–751CrossRefGoogle Scholar
  11. NICE (2013) Guide to the methods of technology appraisal, London, UKGoogle Scholar
  12. NICE (2009) Appraising life-extending, end of life treatments. National Institute for Health and Care Excellence. Available from: http://www.nice.org.uk/guidance/gid-tag387/resources/appraising-life-extending-end-of-life-treatments-paper2
  13. Woods B, Sideris E, Palmer S et al (2017) NICE DSU technical support document 19: partitioned survival analysis for decision modelling in health care—a critical review, Sheffield, UKGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.York Health Economics Consortium, University of YorkYorkUK

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