IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-28410-9.html
   My bibliography  Save this article

Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects

Author

Listed:
  • Deborah Plana

    (Harvard Medical School
    Harvard Medical School and MIT)

  • Geoffrey Fell

    (Dana-Farber Cancer Institute)

  • Brian M. Alexander

    (Dana-Farber Cancer Institute
    Foundation Medicine Inc.)

  • Adam C. Palmer

    (University of North Carolina at Chapel Hill)

  • Peter K. Sorger

    (Harvard Medical School)

Abstract

Individual participant data (IPD) from oncology clinical trials is invaluable for identifying factors that influence trial success and failure, improving trial design and interpretation, and comparing pre-clinical studies to clinical outcomes. However, the IPD used to generate published survival curves are not generally publicly available. We impute survival IPD from ~500 arms of Phase 3 oncology trials (representing ~220,000 events) and find that they are well fit by a two-parameter Weibull distribution. Use of Weibull functions with overall survival significantly increases the precision of small arms typical of early phase trials: analysis of a 50-patient trial arm using parametric forms is as precise as traditional, non-parametric analysis of a 90-patient arm. We also show that frequent deviations from the Cox proportional hazards assumption, particularly in trials of immune checkpoint inhibitors, arise from time-dependent therapeutic effects. Trial duration therefore has an underappreciated impact on the likelihood of success.

Suggested Citation

  • Deborah Plana & Geoffrey Fell & Brian M. Alexander & Adam C. Palmer & Peter K. Sorger, 2022. "Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28410-9
    DOI: 10.1038/s41467-022-28410-9
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-28410-9
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-28410-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Suyu Liu & Ying Yuan, 2015. "Bayesian optimal interval designs for phase I clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(3), pages 507-523, April.
    2. Zhiwei Jiang & Ling Wang & Chanjuan Li & Jielai Xia & Hongxia Jia, 2012. "A Practical Simulation Method to Calculate Sample Size of Group Sequential Trials for Time-to-Event Data under Exponential and Weibull Distribution," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-12, September.
    3. Xiaomin Wan & Liubao Peng & Yuanjian Li, 2015. "A Review and Comparison of Methods for Recreating Individual Patient Data from Published Kaplan-Meier Survival Curves for Economic Evaluations: A Simulation Study," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    4. 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.
    5. Eddie Gibson & Ian Koblbauer & Najida Begum & George Dranitsaris & Danny Liew & Phil McEwan & Amir Abbas Tahami Monfared & Yong Yuan & Ariadna Juarez-Garcia & David Tyas & Michael Lees, 2017. "Modelling the Survival Outcomes of Immuno-Oncology Drugs in Economic Evaluations: A Systematic Approach to Data Analysis and Extrapolation," PharmacoEconomics, Springer, vol. 35(12), pages 1257-1270, December.
    6. Bryan M. Fellman & Ying Yuan, 2015. "Bayesian optimal interval design for phase I oncology clinical trials," Stata Journal, StataCorp LP, vol. 15(1), pages 110-120, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qingyang Liu & Junxian Geng & Frank Fleischer & Qiqi Deng, 2022. "Efficacy-Driven Dose Finding with Toxicity Control in Phase I Oncology Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 413-431, December.
    2. Yimei Li & Ying Yuan, 2020. "PA‐CRM: A continuous reassessment method for pediatric phase I oncology trials with concurrent adult trials," Biometrics, The International Biometric Society, vol. 76(4), pages 1364-1373, December.
    3. Tianjian Zhou & Wentian Guo & Yuan Ji, 2020. "PoD-TPI: Probability-of-Decision Toxicity Probability Interval Design to Accelerate Phase I Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 124-145, July.
    4. Chen Li & Haitao Pan, 2020. "A phase I dose-finding design with incorporation of historical information and adaptive shrinking boundaries," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-18, August.
    5. Xiaodong Luo & Hui Quan, 2020. "Some Meaningful Weighted Log-Rank and Weighted Win Loss Statistics," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 216-224, July.
    6. Kathrin Möllenhoff & Achim Tresch, 2023. "Investigating non-inferiority or equivalence in time-to-event data under non-proportional hazards," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 483-507, July.
    7. Zhang Qingyang, 2023. "A nonparametric test for comparing survival functions based on restricted distance correlation," Dependence Modeling, De Gruyter, vol. 11(1), pages 1-15.
    8. 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.
    9. Grzegorz Wyłupek, 2021. "A permutation test for the two-sample right-censored model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(5), pages 1037-1061, October.
    10. M. Clertant & J. O’Quigley, 2017. "Semiparametric dose finding methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1487-1508, November.
    11. 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.
    12. Yukun Liu & Guosheng Yin, 2017. "Partitioned log-rank tests for the overall homogeneity of hazard rate functions," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(3), pages 400-425, July.
    13. Jimmy T. Efird, 2023. "The Inverse Log-Rank Test: A Versatile Procedure for Late Separating Survival Curves," IJERPH, MDPI, vol. 20(24), pages 1-23, December.
    14. Tamara Fernández & Nicolás Rivera, 2021. "A reproducing kernel Hilbert space log‐rank test for the two‐sample problem," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1384-1432, December.
    15. Jinhui Zhang & Yanlin Shi & Guogui Huang, 2023. "Expected length of stay at residential aged care facilities in Australia: current and future," Journal of Population Research, Springer, vol. 40(4), pages 1-30, December.
    16. Andrea Arfè & Brian Alexander & Lorenzo Trippa, 2021. "Optimality of testing procedures for survival data in the nonproportional hazards setting," Biometrics, The International Biometric Society, vol. 77(2), pages 587-598, June.
    17. Dominic Magirr & Thomas Jaki & Franz Koenig & Martin Posch, 2016. "Sample Size Reassessment and Hypothesis Testing in Adaptive Survival Trials," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-14, February.
    18. Souvik Banerjee & Triparna Bose & Vijay M. Patil & Atanu Bhattacharjee & Kumar Prabhash, 2023. "Bayesian Effective Biological Dose Determination in Immunotherapy Response Trial," Annals of Data Science, Springer, vol. 10(1), pages 209-223, February.
    19. Sandjar Djalalov & Jaclyn Beca & Emmanuel M. Ewara & Jeffrey S. Hoch, 2019. "A Comparison of Different Analysis Methods for Reconstructed Survival Data to Inform Cost‑Effectiveness Analysis," PharmacoEconomics, Springer, vol. 37(12), pages 1525-1536, December.
    20. Bengt Jönsson & Grace Hampson & Jonathan Michaels & Adrian Towse & J.-Matthias Graf Schulenburg & Olivier Wong, 2019. "Advanced therapy medicinal products and health technology assessment principles and practices for value-based and sustainable healthcare," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(3), pages 427-438, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28410-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.