IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v77y2021i4p1315-1327.html
   My bibliography  Save this article

Evaluating multiple surrogate markers with censored data

Author

Listed:
  • Layla Parast
  • Tianxi Cai
  • Lu Tian

Abstract

The utilization of surrogate markers offers the opportunity to reduce the length of required follow‐up time and/or costs of a randomized trial examining the effectiveness of an intervention or treatment. There are many available methods for evaluating the utility of a single surrogate marker including both parametric and nonparametric approaches. However, as the dimension of the surrogate marker increases, a completely nonparametric procedure becomes infeasible due to the curse of dimensionality. In this paper, we define a quantity to assess the value of multiple surrogate markers in a time‐to‐event outcome setting and propose a robust estimation approach for censored data. We focus on surrogate markers that are measured at some landmark time, t0, which occurs earlier than the end of the study. Our approach is based on a dimension reduction procedure with an option to incorporate weights to guard against potential misspecification of the working model, resulting in three different proposed estimators, two of which can be shown to be double robust. We examine the finite sample performance of the estimators under various scenarios using a simulation study. We illustrate the estimation and inference procedures using data from the Diabetes Prevention Program (DPP) to examine multiple potential surrogate markers for diabetes.

Suggested Citation

  • Layla Parast & Tianxi Cai & Lu Tian, 2021. "Evaluating multiple surrogate markers with censored data," Biometrics, The International Biometric Society, vol. 77(4), pages 1315-1327, December.
  • Handle: RePEc:bla:biomet:v:77:y:2021:i:4:p:1315-1327
    DOI: 10.1111/biom.13370
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13370
    Download Restriction: no

    File URL: https://libkey.io/10.1111/biom.13370?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. Ying Huang & Peter B. Gilbert, 2011. "Comparing Biomarkers as Principal Surrogate Endpoints," Biometrics, The International Biometric Society, vol. 67(4), pages 1442-1451, December.
    2. Brenda L. Price & Peter B. Gilbert & Mark J. van der Laan, 2018. "Estimation of the optimal surrogate based on a randomized trial," Biometrics, The International Biometric Society, vol. 74(4), pages 1271-1281, December.
    3. Glynn, Adam N. & Quinn, Kevin M., 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator," Political Analysis, Cambridge University Press, vol. 18(1), pages 36-56, January.
    4. Ariel Alonso & Wim Van der Elst & Geert Molenberghs & Marc Buyse & Tomasz Burzykowski, 2015. "On the relationship between the causal-inference and meta-analytic paradigms for the validation of surrogate endpoints," Biometrics, The International Biometric Society, vol. 71(1), pages 15-24, March.
    5. Layla Parast & Lu Tian & Tianxi Cai, 2014. "Landmark Estimation of Survival and Treatment Effect in a Randomized Clinical Trial," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 384-394, March.
    6. Tyler J. VanderWeele, 2013. "Surrogate Measures and Consistent Surrogates," Biometrics, The International Biometric Society, vol. 69(3), pages 561-565, September.
    7. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    8. Xuan Wang & Layla Parast & Lu Tian & Tianxi Cai, 2020. "Model-free approach to quantifying the proportion of treatment effect explained by a surrogate marker," Biometrika, Biometrika Trust, vol. 107(1), pages 107-122.
    9. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    10. Yeying Zhu & Debashis Ghosh & Donna L. Coffman & Jennifer S. Savage, 2016. "Estimating controlled direct effects of restrictive feeding practices in the ‘Early dieting in girls’ study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(1), pages 115-130, January.
    11. Andrea Rotnitzky & Quanhong Lei & Mariela Sued & James M. Robins, 2012. "Improved double-robust estimation in missing data and causal inference models," Biometrika, Biometrika Trust, vol. 99(2), pages 439-456.
    12. Jane Xu & Scott L. Zeger, 2001. "The Evaluation of Multiple Surrogate Endpoints," Biometrics, The International Biometric Society, vol. 57(1), pages 81-87, March.
    13. Unknown, 1986. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 1(4), pages 1-9.
    14. Yue Wang & Jeremy M. G. Taylor, 2002. "A Measure of the Proportion of Treatment Effect Explained by a Surrogate Marker," Biometrics, The International Biometric Society, vol. 58(4), pages 803-812, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xuan Wang & Layla Parast & Larry Han & Lu Tian & Tianxi Cai, 2023. "Robust approach to combining multiple markers to improve surrogacy," Biometrics, The International Biometric Society, vol. 79(2), pages 788-798, June.

    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. Xuan Wang & Layla Parast & Larry Han & Lu Tian & Tianxi Cai, 2023. "Robust approach to combining multiple markers to improve surrogacy," Biometrics, The International Biometric Society, vol. 79(2), pages 788-798, June.
    2. Layla Parast & Tianxi Cai & Lu Tian, 2023. "Testing for heterogeneity in the utility of a surrogate marker," Biometrics, The International Biometric Society, vol. 79(2), pages 799-810, June.
    3. Iván Díaz & Elizabeth Colantuoni & Daniel F. Hanley & Michael Rosenblum, 2019. "Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 439-468, July.
    4. Guido Imbens & Nathan Kallus & Xiaojie Mao & Yuhao Wang, 2022. "Long-term Causal Inference Under Persistent Confounding via Data Combination," Papers 2202.07234, arXiv.org, revised Aug 2024.
    5. Ying Huang & Shibasish Dasgupta, 2019. "Likelihood-Based Methods for Assessing Principal Surrogate Endpoints in Vaccine Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 504-523, December.
    6. Konan Alain N'Ghauran & Corinne Autant-Bernard, 2020. "Assessing the collaboration and network additionality of innovation policies: a counterfactual approach to the French cluster policy," Post-Print halshs-03128972, HAL.
    7. Antonelli Joseph & Cefalu Matthew, 2020. "Averaging causal estimators in high dimensions," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 92-107, January.
    8. Su, Miaomiao & Wang, Qihua, 2022. "A convex programming solution based debiased estimator for quantile with missing response and high-dimensional covariables," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    9. Jiafeng Chen & David M. Ritzwoller, 2021. "Semiparametric Estimation of Long-Term Treatment Effects," Papers 2107.14405, arXiv.org, revised Aug 2023.
    10. Marianne BLÉHAUT & Xavier D'HAULTFOEUILLE & Jérémy L'HOUR & Alexandre B. TSYBAKOV, 2020. "An alternative to synthetic control for models with many covariates under sparsity," Working Papers 2020-17, Center for Research in Economics and Statistics.
    11. Loh, Wen Wei & Ren, Dongning, 2021. "Data-driven Covariate Selection for Confounding Adjustment by Focusing on the Stability of the Effect Estimator," OSF Preprints yve6u, Center for Open Science.
    12. Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Papers 2206.08503, arXiv.org, revised Apr 2024.
    13. Su, Miaomiao & Wang, Ruoyu & Wang, Qihua, 2022. "A two-stage optimal subsampling estimation for missing data problems with large-scale data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    14. Wang, Qihua & Su, Miaomiao & Wang, Ruoyu, 2021. "A beyond multiple robust approach for missing response problem," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    15. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    16. Aloyce R. Kaliba & Anne G. Gongwe & Kizito Mazvimavi & Ashagre Yigletu, 2021. "Impact of Adopting Improved Seeds on Access to Broader Food Groups Among Small-Scale Sorghum Producers in Tanzania," SAGE Open, , vol. 11(1), pages 21582440209, January.
    17. Karel Vermeulen & Stijn Vansteelandt, 2015. "Bias-Reduced Doubly Robust Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1024-1036, September.
    18. Cousineau, Martin & Verter, Vedat & Murphy, Susan A. & Pineau, Joelle, 2023. "Estimating causal effects with optimization-based methods: A review and empirical comparison," European Journal of Operational Research, Elsevier, vol. 304(2), pages 367-380.
    19. David Cheng & Ashwin N. Ananthakrishnan & Tianxi Cai, 2021. "Robust and efficient semi‐supervised estimation of average treatment effects with application to electronic health records data," Biometrics, The International Biometric Society, vol. 77(2), pages 413-423, June.
    20. Chen, Jiafeng & Ritzwoller, David M., 2023. "Semiparametric estimation of long-term treatment effects," Journal of Econometrics, Elsevier, vol. 237(2).

    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:bla:biomet:v:77:y:2021:i:4:p:1315-1327. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.