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Center-within-trial versus trial-level evaluation of surrogate endpoints

Author

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  • Renfro, Lindsay A.
  • Shi, Qian
  • Xue, Yuan
  • Li, Junlong
  • Shang, Hongwei
  • Sargent, Daniel J.

Abstract

Evaluation of candidate surrogate endpoints using individual patient data from multiple clinical trials is considered the gold standard approach to validate surrogates at both patient and trial levels. However, this approach assumes the availability of patient-level data from a relatively large collection of similar trials, which may not be possible to achieve for a given disease application. One common solution to the problem of too few similar trials involves performing trial-level surrogacy analyses on trial sub-units (e.g., centers within trials), thereby artificially increasing the trial-level sample size for feasibility of the multi-trial analysis. To date, the practical impact of treating trial sub-units (centers) identically to trials in multi-trial surrogacy analyses remains unexplored, and conditions under which this ad hoc solution may in fact be reasonable have not been identified. We perform a simulation study to identify such conditions, and demonstrate practical implications using a multi-trial dataset of patients with early stage colon cancer.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:78:y:2014:i:c:p:1-20
    DOI: 10.1016/j.csda.2014.03.011
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    References listed on IDEAS

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    1. Shi, Qian & Renfro, Lindsay A. & Bot, Brian M. & Burzykowski, Tomasz & Buyse, Marc & Sargent, Daniel J., 2011. "Comparative assessment of trial-level surrogacy measures for candidate time-to-event surrogate endpoints in clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2748-2757, September.
    2. Didier Renard & Helena Geys & Geert Molenberghs & Tomasz Burzykowski & Marc Buyse & Tony Vangeneugden & Luc Bijnens, 2003. "Validation of a longitudinally measured surrogate marker for a time-to-event endpoint," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(2), pages 235-247.
    3. Abrahantes, Jose Cortinas & Molenberghs, Geert & Burzykowski, Tomasz & Shkedy, Ziv & Abad, Ariel Alonso & Renard, Didier, 2004. "Choice of units of analysis and modeling strategies in multilevel hierarchical models," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 537-563, October.
    4. Ariel Alonso & Geert Molenberghs & Tomasz Burzykowski & Didier Renard & Helena Geys & Ziv Shkedy & Fabián Tibaldi & José Cortiñas Abrahantes & Marc Buyse, 2004. "Prentice's Approach and the Meta-Analytic Paradigm: A Reflection on the Role of Statistics in the Evaluation of Surrogate Endpoints," Biometrics, The International Biometric Society, vol. 60(3), pages 724-728, September.
    5. 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.
    6. Ariel Alonso & Geert Molenberghs, 2007. "Surrogate Marker Evaluation from an Information Theory Perspective," Biometrics, The International Biometric Society, vol. 63(1), pages 180-186, March.
    7. 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.
    8. Tilahun, Abel & Pryseley, Assam & Alonso, Ariel & Molenberghs, Geert, 2007. "Flexible surrogate marker evaluation from several randomized clinical trials with continuous endpoints, using R and SAS," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4152-4163, May.
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