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Assessing Treatment Benefit in Immuno-oncology

Author

Listed:
  • Marc Buyse

    (International Drug Development Institute
    Hasselt University)

  • Everardo D. Saad

    (International Drug Development Institute)

  • Tomasz Burzykowski

    (Hasselt University
    International Drug Development Institute)

  • Julien Péron

    (Hospices Civils de Lyon
    University of Lyon 1, CNRS UMR 5558, Biometry and Evolutive Biology Laboratory, Biostatistics-Health Team)

Abstract

Immuno-oncology is a buoyant field of research, with recently developed drugs showing unprecedented response rates and/or a hope for a meaningful prolongation of the overall survival of some patients. These promising clinical developments have also pointed to the need of adapting statistical methods to best describe and test for treatment effects in randomized clinical trials. We review adaptations to tumor response and progression criteria for immune therapies. Survival may be the endpoint of choice for clinical trials in some tumor types, and the search for surrogate endpoints is likely to continue to try and reduce the duration and size of clinical trials. In situations for which hazards are likely to be non-proportional, weighted logrank tests may be preferred as they have substantially more power to detect late separation of survival curves. Alternatively, there is currently much interest in accelerated failure time models, and in capturing treatment effect by the difference in restricted mean survival times between randomized groups. Finally, generalized pairwise comparisons offer much promise in the field of immuno-oncology, both to detect late emerging treatment effects and as a general approach to personalize treatment choices through a benefit/risk approach.

Suggested Citation

  • Marc Buyse & Everardo D. Saad & Tomasz Burzykowski & Julien Péron, 2020. "Assessing Treatment Benefit in Immuno-oncology," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 83-103, July.
    • Handle: RePEc:spr:stabio:v:12:y:2020:i:2:d:10.1007_s12561-020-09268-1
    DOI: 10.1007/s12561-020-09268-1
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    References listed on IDEAS

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    1. Lu Tian & Haoda Fu & Stephen J. Ruberg & Hajime Uno & Lee†Jen Wei, 2018. "Efficiency of two sample tests via the restricted mean survival time for analyzing event time observations," Biometrics, The International Biometric Society, vol. 74(2), pages 694-702, June.
    2. Nan Xuan Lin & Stuart Logan & William Edward Henley, 2013. "Bias and Sensitivity Analysis When Estimating Treatment Effects from the Cox Model with Omitted Covariates," Biometrics, The International Biometric Society, vol. 69(4), pages 850-860, December.
    3. Chiou, Sy Han & Kang, Sangwook & Yan, Jun, 2014. "Fitting Accelerated Failure Time Models in Routine Survival Analysis with R Package aftgee," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i11).
    4. Theodore Karrison, 2016. "Versatile tests for comparing survival curves based on weighted logrank statistics," 2016 Stata Conference 20, Stata Users Group.
    5. Song Yang & Ross Prentice, 2010. "Improved Logrank-Type Tests for Survival Data Using Adaptive Weights," Biometrics, The International Biometric Society, vol. 66(1), pages 30-38, March.
    6. Theodore G. Karrison, 2016. "Versatile tests for comparing survival curves based on weighted log-rank statistics," Stata Journal, StataCorp LP, vol. 16(3), pages 678-690, September.
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    Cited by:

    1. Bo Huang & Naitee Ting, 2020. "Introduction to Special Issue on ‘Statistical Methods for Cancer Immunotherapy’," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 79-82, July.

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