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Parametric Survival Extrapolation of Early Survival Data in Economic Analyses: A Comparison of Projected Versus Observed Updated Survival

  • Louis Everest
    Affiliations
    Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
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  • Scott Blommaert
    Affiliations
    Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
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  • Ryan W. Chu
    Affiliations
    Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
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  • Author Footnotes
    ∗ Kelvin K.W. Chan and Ambica Parmar contributed equally to the present study.
    Kelvin K.W. Chan
    Correspondence
    Correspondence: Kelvin K.W. Chan, MD, PhD, Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, Ontario, Canada M4N 3M5.
    Footnotes
    ∗ Kelvin K.W. Chan and Ambica Parmar contributed equally to the present study.
    Affiliations
    Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada

    University of Toronto, Toronto, Ontario, Canada

    Canadian Centre for Applied Research in Cancer Control, Toronto, Ontario, Canada

    Cancer Care Ontario, Toronto, Ontario, Canada
    Search for articles by this author
  • Author Footnotes
    ∗ Kelvin K.W. Chan and Ambica Parmar contributed equally to the present study.
    Ambica Parmar
    Footnotes
    ∗ Kelvin K.W. Chan and Ambica Parmar contributed equally to the present study.
    Affiliations
    Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada

    University of Toronto, Toronto, Ontario, Canada
    Search for articles by this author
  • Author Footnotes
    ∗ Kelvin K.W. Chan and Ambica Parmar contributed equally to the present study.
Published:November 24, 2021DOI:https://doi.org/10.1016/j.jval.2021.10.004

      Highlights

      • Recent literature has highlighted concerns that parametric survival models may misrepresent long-term survival benefits, particularly with novel cancer therapeutics that putatively preserve a “cured” populations, such as is seen with immunotherapy. Presently, there is a gap in literature examining the biasness and precision of parametric survival estimates based on primary publications compared with the survival estimates based on the updated survival publications.
      • The present analysis indicated substantial imprecision in the projected survival based on initial data compared with the updated survival data. In addition, as the time extrapolated increased, the projected survival estimate was observed to be less precise. These results address questions presented in literature regarding the reliability of conventional parametric models to capture the underlying hazard function of novel cancer agents, based on the limited follow-up time for survival and censoring distribution of primary publications.
      • Health technology assessment committees need to be aware of this uncertainty in the estimation of incremental effectiveness and the resultant cost-effectiveness conclusions when making reimbursement decisions based on initial publication with immature survival data. In addition, the results of the present study highlight the importance of reassessment frameworks, for public payers and policy decision makers to incorporate updated survival data into funding decisions.

      Abstract

      Objectives

      To establish the value of cancer drugs by cost-effectiveness analysis, lifetime parametric survival extrapolations are often fitted to early data. Recent literature suggests that the benefit of cancer agents in primary publications is often different compared with updated data. This study aimed to examine the projected survival based on parametric extrapolations compared with observed survival based on updated data.

      Methods

      US Food and Drug Administration oncology approvals from January 2006 to December 2015 were reviewed to identify randomized controlled trials, with updated overall survival (OS) or progression-free survival (PFS) data within 5 years. Individual patient data were reconstructed using established methods on initial and updated publications. Projected survival was calculated as the best-fit parametric restricted mean survival time (RMST) based on extrapolated initial Kaplan-Meier curves whereas observed survival was calculated as observed RMST based on updated Kaplan-Meier curves. Mean deviations, mean absolute error (MAE), mean absolute percentage error, and linear regressions were conducted to examine the relationship between projected and observed survival.

      Results

      In total, 32 randomized controlled trials were included. The MAE between the projected RMST and observed RMST was 3.18 months (OS) and 2.84 months (PFS) and absolute percentage error of 100% (OS) and 23% (PFS), suggesting substantial imprecision of the projected RMST in predicting the updated RMST. The linear regression indicated MAE increased as time extrapolated and as the percentage of censored patients increased.

      Conclusions

      This study demonstrated substantial difference in projected survival between initial and updated publications. Health technology assessment committees need to be aware of the potential uncertainty of incremental effectiveness and resultant value-for-money assessment when making reimbursement decisions based on initial publications with immature survival data.

      Keywords

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