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The Evaluation of Multiple Surrogate Endpoints

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  • Jane Xu
  • Scott L. Zeger

Abstract

Summary. Surrogate endpoints are desirable because they typically result in smaller, faster efficacy studies compared with the ones using the clinical endpoints. Research on surrogate endpoints has received substantial attention lately, but most investigations have focused on the validity of using a single biomarker as a surrogate. Our paper studies whether the use of multiple markers can improve inferences about a treatment's effects on a clinical endpoint. We propose a joint model for a time to clinical event and for repeated measures over time on multiple biomarkers that are potential surrogates. This model extends the formulation of Xu and Zeger (2001, in press) and Fawcett and Thomas (1996, Statistics in Medicine15, 1663–1685). We propose two complementary measures of the relative benefit of multiple surrogates as opposed to a single one. Markov chain Monte Carlo is implemented to estimate model parameters. The methodology is illustrated with an analysis of data from a schizophrenia clinical trial.

Suggested Citation

  • Jane Xu & Scott L. Zeger, 2001. "The Evaluation of Multiple Surrogate Endpoints," Biometrics, The International Biometric Society, vol. 57(1), pages 81-87, March.
    • Handle: RePEc:bla:biomet:v:57:y:2001:i:1:p:81-87
    DOI: 10.1111/j.0006-341X.2001.00081.x
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    Cited by:

    1. Wen Ye & Xihong Lin & Jeremy M. G. Taylor, 2008. "Semiparametric Modeling of Longitudinal Measurements and Time-to-Event Data–A Two-Stage Regression Calibration Approach," Biometrics, The International Biometric Society, vol. 64(4), pages 1238-1246, December.
    2. 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.
    3. Elizabeth R. Brown & Joseph G. Ibrahim, 2003. "Bayesian Approaches to Joint Cure-Rate and Longitudinal Models with Applications to Cancer Vaccine Trials," Biometrics, The International Biometric Society, vol. 59(3), pages 686-693, September.
    4. Yueh-Yun Chi & Joseph G. Ibrahim, 2006. "Joint Models for Multivariate Longitudinal and Multivariate Survival Data," Biometrics, The International Biometric Society, vol. 62(2), pages 432-445, June.
    5. Tang, Nian-Sheng & Tang, An-Min & Pan, Dong-Dong, 2014. "Semiparametric Bayesian joint models of multivariate longitudinal and survival data," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 113-129.
    6. Loucks, Eric B. & Juster, Robert P. & Pruessner, Jens C., 2008. "Neuroendocrine biomarkers, allostatic load, and the challenge of measurement: A commentary on Gersten," Social Science & Medicine, Elsevier, vol. 66(3), pages 525-530, February.
    7. Chen, Ming-Hui & Ibrahim, Joseph G. & Sinha, Debajyoti, 2004. "A new joint model for longitudinal and survival data with a cure fraction," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 18-34, October.
    8. Jiawei Xu & Matthew A. Psioda & Joseph G. Ibrahim, 2023. "Bayesian Design of Clinical Trials Using Joint Cure Rate Models for Longitudinal and Time-to-Event Data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 213-233, January.
    9. Jaeun Choi & Jianwen Cai & Donglin Zeng, 2017. "Penalized Likelihood Approach for Simultaneous Analysis of Survival Time and Binary Longitudinal Outcome," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 79(2), pages 190-216, November.
    10. Jiafeng Chen & David M. Ritzwoller, 2021. "Semiparametric Estimation of Long-Term Treatment Effects," Papers 2107.14405, arXiv.org, revised Aug 2023.
    11. David B. Dunson & M. Watson & Jack A. Taylor, 2003. "Bayesian Latent Variable Models for Median Regression on Multiple Outcomes," Biometrics, The International Biometric Society, vol. 59(2), pages 296-304, June.
    12. Angela Dobson & Robin Henderson, 2003. "Diagnostics for Joint Longitudinal and Dropout Time Modeling," Biometrics, The International Biometric Society, vol. 59(4), pages 741-751, December.
    13. Feng Gao & J. Miller & Chengjie Xiong & Julia Beiser & Mae Gordon, 2011. "A joint-modeling approach to assess the impact of biomarker variability on the risk of developing clinical outcome," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 83-100, March.
    14. Hongtu Zhu & Joseph G. Ibrahim & Yueh-Yun Chi & Niansheng Tang, 2012. "Bayesian Influence Measures for Joint Models for Longitudinal and Survival Data," Biometrics, The International Biometric Society, vol. 68(3), pages 954-964, September.
    15. Lisa M. McCrink & Adele H. Marshall & Karen J. Cairns, 2013. "Advances in Joint Modelling: A Review of Recent Developments with Application to the Survival of End Stage Renal Disease Patients," International Statistical Review, International Statistical Institute, vol. 81(2), pages 249-269, August.
    16. 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.

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