IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v43y2023i5p539-552.html
   My bibliography  Save this article

Using Bayesian Evidence Synthesis Methods to Incorporate Real-World Evidence in Surrogate Endpoint Evaluation

Author

Listed:
  • Lorna Wheaton

    (Biostatistics Research Group, Department of Health Sciences, University of Leicester, UK)

  • Anastasios Papanikos

    (Biostatistics Research Group, Department of Health Sciences, University of Leicester, UK
    GlaxoSmithKline R&D Centre, GlaxoSmithKline, Stevenage, UK)

  • Anne Thomas

    (Leicester Cancer Research Centre, University of Leicester, Leicester, UK)

  • Sylwia Bujkiewicz

    (Biostatistics Research Group, Department of Health Sciences, University of Leicester, UK)

Abstract

Objective Traditionally, validation of surrogate endpoints has been carried out using randomized controlled trial (RCT) data. However, RCT data may be too limited to validate surrogate endpoints. In this article, we sought to improve the validation of surrogate endpoints with the inclusion of real-world evidence (RWE). Methods We use data from comparative RWE (cRWE) and single-arm RWE (sRWE) to supplement RCT evidence for the evaluation of progression-free survival (PFS) as a surrogate endpoint to overall survival (OS) in metastatic colorectal cancer (mCRC). Treatment effect estimates from RCTs, cRWE, and matched sRWE, comparing antiangiogenic treatments with chemotherapy, were used to inform surrogacy patterns and predictions of the treatment effect on OS from the treatment effect on PFS. Results Seven RCTs, 4 cRWE studies, and 2 matched sRWE studies were identified. The addition of RWE to RCTs reduced the uncertainty around the estimates of the parameters for the surrogate relationship. The addition of RWE to RCTs also improved the accuracy and precision of predictions of the treatment effect on OS obtained using data on the observed effect on PFS. Conclusion The addition of RWE to RCT data improved the precision of the parameters describing the surrogate relationship between treatment effects on PFS and OS and the predicted clinical benefit of antiangiogenic therapies in mCRC. Highlights Regulatory agencies increasingly rely on surrogate endpoints when making licensing decisions, and for the decisions to be robust, surrogate endpoints need to be validated. In the era of precision medicine, when surrogacy patterns may depend on the drug’s mechanism of action and trials of targeted therapies may be small, data from randomized controlled trials may be limited. Real-world evidence (RWE) is increasingly used at different stages of the drug development process. When used to enhance the evidence base for surrogate endpoint evaluation, RWE can improve inferences about the strength of surrogate relationships and the precision of predicted treatment effect on the final clinical outcome based on the observed effect on the surrogate endpoint in a new trial. Careful selection of RWE is needed to reduce risk of bias.

Suggested Citation

  • Lorna Wheaton & Anastasios Papanikos & Anne Thomas & Sylwia Bujkiewicz, 2023. "Using Bayesian Evidence Synthesis Methods to Incorporate Real-World Evidence in Surrogate Endpoint Evaluation," Medical Decision Making, , vol. 43(5), pages 539-552, July.
    • Handle: RePEc:sae:medema:v:43:y:2023:i:5:p:539-552
    DOI: 10.1177/0272989X231162852
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X231162852
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X231162852?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. Yinghui Wei & Patrick Royston, 2017. "Reconstructing time-to-event data from published Kaplan–Meier curves," Stata Journal, StataCorp LP, vol. 17(4), pages 786-802, December.
    2. Sturtz, Sibylle & Ligges, Uwe & Gelman, Andrew, 2005. "R2WinBUGS: A Package for Running WinBUGS from R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i03).
    3. Tomasz Burzykowski & Geert Molenberghs & Marc Buyse & Helena Geys & Didier Renard, 2001. "Validation of surrogate end points in multiple randomized clinical trials with failure time end points," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(4), pages 405-422.
    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. Lachaud, Michée A. & Bravo-Ureta, Boris E., 2022. "A Bayesian statistical analysis of return to agricultural R&D investment in Latin America: Implications for food security," Technology in Society, Elsevier, vol. 70(C).
    2. Erin E. Gabriel & Michael J. Daniels & M. Elizabeth Halloran, 2016. "Comparing biomarkers as trial level general surrogates," Biometrics, The International Biometric Society, vol. 72(4), pages 1046-1054, December.
    3. Liang, Zhongyao & Qian, Song S. & Wu, Sifeng & Chen, Huili & Liu, Yong & Yu, Yanhong & Yi, Xuan, 2019. "Using Bayesian change point model to enhance understanding of the shifting nutrients-phytoplankton relationship," Ecological Modelling, Elsevier, vol. 393(C), pages 120-126.
    4. Marc Marí-Dell’Olmo & Miguel Ángel Martínez-Beneito, 2015. "A Multilevel Regression Model for Geographical Studies in Sets of Non-Adjacent Cities," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-12, August.
    5. Casimir Ledoux Sofeu & Virginie Rondeau, 2020. "How to use frailtypack for validating failure-time surrogate endpoints using individual patient data from meta-analyses of randomized controlled trials," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-25, January.
    6. Ariel Alonso & Helena Geys & Geert Molenberghs & Michael G. Kenward & Tony Vangeneugden, 2004. "Validation of Surrogate Markers in Multiple Randomized Clinical Trials with Repeated Measurements: Canonical Correlation Approach," Biometrics, The International Biometric Society, vol. 60(4), pages 845-853, December.
    7. Zhao, Qing & Boomer, G. Scott & Silverman, Emily & Fleming, Kathy, 2017. "Accounting for the temporal variation of spatial effect improves inference and projection of population dynamics models," Ecological Modelling, Elsevier, vol. 360(C), pages 252-259.
    8. Arielle Anderer & Hamsa Bastani & John Silberholz, 2022. "Adaptive Clinical Trial Designs with Surrogates: When Should We Bother?," Management Science, INFORMS, vol. 68(3), pages 1982-2002, March.
    9. Hirofumi Michimae & Takeshi Emura, 2022. "Bayesian ridge estimators based on copula-based joint prior distributions for regression coefficients," Computational Statistics, Springer, vol. 37(5), pages 2741-2769, November.
    10. Renfro, Lindsay A. & Shi, Qian & Xue, Yuan & Li, Junlong & Shang, Hongwei & Sargent, Daniel J., 2014. "Center-within-trial versus trial-level evaluation of surrogate endpoints," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 1-20.
    11. Bo-Hong Wu & Hirofumi Michimae & Takeshi Emura, 2020. "Meta-analysis of individual patient data with semi-competing risks under the Weibull joint frailty–copula model," Computational Statistics, Springer, vol. 35(4), pages 1525-1552, December.
    12. Adrian D Vickers & Claire Ainsworth & Reema Mody & Annika Bergman & Caroline S Ling & Jasmina Medjedovic & Michael Smyth, 2016. "Systematic Review with Network Meta-Analysis: Comparative Efficacy of Biologics in the Treatment of Moderately to Severely Active Ulcerative Colitis," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-21, October.
    13. Marco Gramatica & Peter Congdon & Silvia Liverani, 2021. "Bayesian modelling for spatially misaligned health areal data: A multiple membership approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 645-666, June.
    14. Tomasz Burzykowski & Geert Molenberghs & Marc Buyse, 2004. "The validation of surrogate end points by using data from randomized clinical trials: a case‐study in advanced colorectal cancer," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(1), pages 103-124, February.
    15. Yong Li & Zeng Tao & Jun Yu, "undated". "Robust Deviance Information Criterion for Latent Variable Models," Working Papers CoFie-04-2012, Singapore Management University, Sim Kee Boon Institute for Financial Economics.
    16. repec:jss:jstsof:36:c01 is not listed on IDEAS
    17. Abadi, Fitsum & Gimenez, Olivier & Jakober, Hans & Stauber, Wolfgang & Arlettaz, Raphaël & Schaub, Michael, 2012. "Estimating the strength of density dependence in the presence of observation errors using integrated population models," Ecological Modelling, Elsevier, vol. 242(C), pages 1-9.
    18. Będowska-Sójka, Barbara & Kliber, Agata, 2022. "Can cryptocurrencies hedge oil price fluctuations? A pandemic perspective," Energy Economics, Elsevier, vol. 115(C).
    19. Earl W Duncan & Kerrie L Mengersen, 2020. "Comparing Bayesian spatial models: Goodness-of-smoothing criteria for assessing under- and over-smoothing," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-28, May.
    20. repec:jss:jstsof:40:i05 is not listed on IDEAS
    21. Guy Abel & Jakub Bijak & Jonathan J. Forster & James Raymer & Peter W.F. Smith & Jackie S.T. Wong, 2013. "Integrating uncertainty in time series population forecasts: An illustration using a simple projection model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(43), pages 1187-1226.
    22. Yong Li & Zhongxin Ni & Jie Zhang, 2011. "An Efficient Stochastic Simulation Algorithm for Bayesian Unit Root Testing in Stochastic Volatility Models," Computational Economics, Springer;Society for Computational Economics, vol. 37(3), pages 237-248, March.

    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:sae:medema:v:43:y:2023:i:5:p:539-552. 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: SAGE Publications (email available below). General contact details of provider: .

    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.