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Extrapolation of Survival Curves from Cancer Trials Using External Information

Author

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  • Patricia Guyot
  • Anthony E. Ades
  • Matthew Beasley
  • Béranger Lueza
  • Jean-Pierre Pignon
  • Nicky J. Welton

Abstract

Background: Estimates of life expectancy are a key input to cost-effectiveness analysis (CEA) models for cancer treatments. Due to the limited follow-up in Randomized Controlled Trials (RCTs), parametric models are frequently used to extrapolate survival outcomes beyond the RCT period. However, different parametric models that fit the RCT data equally well may generate highly divergent predictions of treatment-related gain in life expectancy. Here, we investigate the use of information external to the RCT data to inform model choice and estimation of life expectancy. Methods: We used Bayesian multi-parameter evidence synthesis to combine the RCT data with external information on general population survival, conditional survival from cancer registry databases, and expert opinion. We illustrate with a 5-year follow-up RCT of cetuximab plus radiotherapy v. radiotherapy alone for head and neck cancer. Results: Standard survival time distributions were insufficiently flexible to simultaneously fit both the RCT data and external data on general population survival. Using spline models, we were able to estimate a model that was consistent with the trial data and all external data. A model integrating all sources achieved an adequate fit and predicted a 4.7-month (95% CrL: 0.4; 9.1) gain in life expectancy due to cetuximab. Conclusions: Long-term extrapolation using parametric models based on RCT data alone is highly unreliable and these models are unlikely to be consistent with external data. External data can be integrated with RCT data using spline models to enable long-term extrapolation. Conditional survival data could be used for many cancers and general population survival may have a role in other conditions. The use of external data should be guided by knowledge of natural history and treatment mechanisms.

Suggested Citation

  • Patricia Guyot & Anthony E. Ades & Matthew Beasley & Béranger Lueza & Jean-Pierre Pignon & Nicky J. Welton, 2017. "Extrapolation of Survival Curves from Cancer Trials Using External Information," Medical Decision Making, , vol. 37(4), pages 353-366, May.
    • Handle: RePEc:sae:medema:v:37:y:2017:i:4:p:353-366
    DOI: 10.1177/0272989X16670604
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    References listed on IDEAS

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    1. P. C. Lambert & P. W. Dickman & C. L. Weston & J. R. Thompson, 2010. "Estimating the cure fraction in population‐based cancer studies by using finite mixture models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 35-55, January.
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    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. M. Campioni & I. Agirrezabal & R. Hajek & J. Minarik & L. Pour & I. Spicka & S. Gonzalez-McQuire & P. Jandova & V. Maisnar, 2020. "Methodology and results of real-world cost-effectiveness of carfilzomib in combination with lenalidomide and dexamethasone in relapsed multiple myeloma using registry data," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(2), pages 219-233, March.
    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. Alexina J. Mason & Manuel Gomes & James Carpenter & Richard Grieve, 2021. "Flexible Bayesian longitudinal models for cost‐effectiveness analyses with informative missing data," Health Economics, John Wiley & Sons, Ltd., vol. 30(12), pages 3138-3158, December.
    5. Philip Cooney & Arthur White, 2023. "Direct Incorporation of Expert Opinion into Parametric Survival Models to Inform Survival Extrapolation," Medical Decision Making, , vol. 43(3), pages 325-336, April.
    6. M. A. Chaudhary & M. Edmondson-Jones & G. Baio & E. Mackay & J. R. Penrod & D. J. Sharpe & G. Yates & S. Rafiq & K. Johannesen & M. K. Siddiqui & J. Vanderpuye-Orgle & A. Briggs, 2023. "Use of Advanced Flexible Modeling Approaches for Survival Extrapolation from Early Follow-up Data in two Nivolumab Trials in Advanced NSCLC with Extended Follow-up," Medical Decision Making, , vol. 43(1), pages 91-109, January.
    7. 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.
    8. Ash Bullement & Mark Edmondson-Jones & Patricia Guyot & Nicky J. Welton & Gianluca Baio & Matthew Stevenson & Nicholas R. Latimer, 2024. "MPES-R: Multi-Parameter Evidence Synthesis in R for Survival Extrapolation—A Tutorial," PharmacoEconomics, Springer, vol. 42(12), pages 1317-1327, December.
    9. 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.
    10. Taihang Shao & Mingye Zhao & Leyi Liang & Lizheng Shi & Wenxi Tang, 2023. "Impact of Extrapolation Model Choices on the Structural Uncertainty in Economic Evaluations for Cancer Immunotherapy: A Case Study of Checkmate 067," PharmacoEconomics - Open, Springer, vol. 7(3), pages 383-392, May.

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