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A Review of Survival Analysis Methods Used in NICE Technology Appraisals of Cancer Treatments: Consistency, Limitations, and Areas for Improvement

Author

Listed:
  • Helen Bell Gorrod

    (School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK)

  • Ben Kearns

    (School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK)

  • John Stevens

    (School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK)

  • Praveen Thokala

    (School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK)

  • Alexander Labeit

    (School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
    Saw Swee Hock School of Public Health, National University of Singapore)

  • Nicholas Latimer

    (School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK)

  • David Tyas

    (Bristol-Myers Squibb, Uxbridge, Hillingdon, UK)

  • Ahmed Sowdani

    (Bristol-Myers Squibb, Uxbridge, Hillingdon, UK)

Abstract

Objectives. In June 2011, the National Institute for Health and Care Excellence (NICE) Decision Support Unit published a Technical Support Document (TSD) providing recommendations on survival analysis for NICE technology appraisals (TAs). Survival analysis outputs are influential inputs into economic models estimating the cost-effectiveness of new cancer treatments. Hence, it is important that systematic and justifiable model selection approaches are used. This study investigates the extent to which the TSD recommendations have been followed since its publication. Methods. We reviewed NICE cancer TAs completed between July 2011 and July 2017. Information on survival analyses undertaken and associated critiques for overall survival (OS) and progression-free survival were extracted from the company submissions, Evidence Review Group (ERG) reports, and final appraisal determination documents. Results. Information was extracted from 58 TAs. Only 4 (7%) followed all TSD recommendations for OS outcomes. The vast majority (91%) compared a range of common parametric models and assessed their fit to the data (86%). Only a minority of TAs included an assessment of the shape of the hazard function (38%) or proportional hazards assumption (40%). Validation of the extrapolated portion of the survival function using external data was attempted in a minority of TAs (40%). Extrapolated survival functions were frequently criticized by ERGs (71%). Conclusions. Survival analysis within NICE TAs remains suboptimal, despite publication of the TSD. Model selection is not undertaken in a systematic way, resulting in inconsistencies between TAs. More attention needs to be given to assessing hazard functions and validation of extrapolated survival functions. Novel methods not described in the TSD have been used, particularly in the context of immuno-oncology, suggesting that an updated TSD may be of value.

Suggested Citation

  • Helen Bell Gorrod & Ben Kearns & John Stevens & Praveen Thokala & Alexander Labeit & Nicholas Latimer & David Tyas & Ahmed Sowdani, 2019. "A Review of Survival Analysis Methods Used in NICE Technology Appraisals of Cancer Treatments: Consistency, Limitations, and Areas for Improvement," Medical Decision Making, , vol. 39(8), pages 899-909, November.
    • Handle: RePEc:sae:medema:v:39:y:2019:i:8:p:899-909
    DOI: 10.1177/0272989X19881967
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    References listed on IDEAS

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    1. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
    2. K. Ishak & Noemi Kreif & Agnes Benedict & Noemi Muszbek, 2013. "Overview of Parametric Survival Analysis for Health-Economic Applications," PharmacoEconomics, Springer, vol. 31(8), pages 663-675, August.
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    Cited by:

    1. Nicholas R. Latimer & Kurt Taylor & Anthony J. Hatswell & Sophia Ho & Gabriel Okorogheye & Clara Chen & Inkyu Kim & John Borrill & David Bertwistle, 2024. "An Evaluation of an Algorithm for the Selection of Flexible Survival Models for Cancer Immunotherapies: Pass or Fail?," PharmacoEconomics, Springer, vol. 42(12), pages 1395-1412, December.
    2. Nicholas R. Latimer & Mark J. Rutherford, 2024. "Mixture and Non-mixture Cure Models for Health Technology Assessment: What You Need to Know," PharmacoEconomics, Springer, vol. 42(10), pages 1073-1090, October.
    3. Ash Bullement & Matthew D. Stevenson & Gianluca Baio & Gemma E. Shields & Nicholas R. Latimer, 2023. "A Systematic Review of Methods to Incorporate External Evidence into Trial-Based Survival Extrapolations for Health Technology Assessment," Medical Decision Making, , vol. 43(5), pages 610-620, July.
    4. Daniel Gallacher & Peter Kimani & Nigel Stallard, 2021. "Extrapolating Parametric Survival Models in Health Technology Assessment: A Simulation Study," Medical Decision Making, , vol. 41(1), pages 37-50, January.
    5. Zhaojing Che & Nathan Green & Gianluca Baio, 2023. "Blended Survival Curves: A New Approach to Extrapolation for Time-to-Event Outcomes from Clinical Trials in Health Technology Assessment," Medical Decision Making, , vol. 43(3), pages 299-310, April.
    6. Daniel Gallacher & Peter Kimani & Nigel Stallard, 2022. "Biased Survival Predictions When Appraising Health Technologies in Heterogeneous Populations," PharmacoEconomics, Springer, vol. 40(1), pages 109-120, January.

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