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Evaluation of Surrogate Endpoints Using a Meta-Analysis Approach with Individual Patient Data: Summary of a Gastric Cancer Meta-Analysis Project

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Frontiers of Biostatistical Methods and Applications in Clinical Oncology

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Abstract

Statistical methodologies for evaluation of surrogate endpoints have been developed actively since 1989. A meta-analytic approach is frequently applied with data from several randomized controlled trials, and the surrogacy measures are evaluated at the individual level and at the trial level. This approach needs individual patient data for each trial and requires collaborative work with several professionals. In this chapter, we introduce the Global Advanced/Adjuvant Stomach Tumor Research International Collaboration (GASTRIC) project, which is an academic, worldwide project that conducts individual patient data meta-analyses of randomized controlled trials of post-operative adjuvant chemotherapy for resectable gastric cancer or chemotherapy for advanced/recurrent gastric cancer. We describe our statistical method for the evaluation of surrogate endpoints. In particular, we focus on the practical aspects of group establishment, data collection, and data analysis. Finally, future perspectives for the evaluation of surrogate endpoints are discussed.

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References

  1. Alonso A, Van der Elst W, Molenberghs G, Buyse M, Burzykowski T, et al. On the relationship between the causal-inference and meta-analytic paradigms for the validation of surrogate endpoints. Biometrics. 2015;71:15–24.

    Article  MathSciNet  MATH  Google Scholar 

  2. Alonso A, Molenberghs G. Surrogate marker evaluation from an information theory perspective. Biometrics. 2007;63:180–6.

    Article  MathSciNet  MATH  Google Scholar 

  3. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69:89–95.

    Article  Google Scholar 

  4. Bonini S, Eichler H-G, Wathion N, Rasi G. Transparency and the European Medicines Agency—sharing of clinical trial data. New Engl J Med. 2014;371:2450–2.

    Article  Google Scholar 

  5. Broglio KR, Berry DA. Detecting an overall survival benefit that is derived from progression-free survival. J Natl Cancer Inst. 2009;101:1642–9.

    Article  Google Scholar 

  6. Burzykowski T, Buyse M. Surrogate threshold effect: an alternative measure for meta-analytic surrogate endpoint validation. Pharm Stat. 2006;5:173–86.

    Article  Google Scholar 

  7. Burzykowski T, Buyse M, Piccart-Gebhart MJ, Sledge G, Carmichael J, Lu H, et al. Evaluation of tumor response, disease control, progression-free survival, and time to progression as potential surrogate end points in metastatic breast cancer. J Clin Oncol. 2008;26:1987–92.

    Article  Google Scholar 

  8. Burzykowski T, Molenberghs G, Buyse M. The evaluation of surrogate endpoints. New York: Springer; 2006.

    MATH  Google Scholar 

  9. Burzykowski T, Molenberghs G, Buyse M, Geys H, Renard D. Validation of surrogate end points in multiple randomized clinical trials with failure time end points. J R Stat Soc C. 2001;50:405–22.

    Article  MathSciNet  MATH  Google Scholar 

  10. Buyse M. Contributions of meta-analyses based on individual patient data to therapeutic progress in colorectal cancer. Int J Clin Oncol. 2009;14:95–101.

    Article  Google Scholar 

  11. Buyse M, Burzykowski T, Carroll K, Michiels S, Sargent DJ, Miller LL, et al. Progression-free survival is a surrogate for survival in advanced colorectal cancer. J Clin Oncol. 2007;25:5218–24.

    Article  Google Scholar 

  12. Buyse M, Burzykowski T, Michiels S, Carroll K. Individual- and trial-level surrogacy in colorectal cancer. Stat Methods Med Res. 2008;17:467–75.

    Article  MathSciNet  Google Scholar 

  13. Buyse M, Molenberghs G. Criteria for the validation of surrogate endpoints in randomized experiments. Biometrics. 1998;54:1014–2109.

    Article  MATH  Google Scholar 

  14. Buyse M, Molenberghs G, Burzykowski T, Renard D, Geys H. The validation of surrogate endpoints in meta-analyses of randomized experiments. Biostatistics. 2000;1:49–67.

    Article  MATH  Google Scholar 

  15. Buyse M, Molenberghs G, Paoletti X, Oba K, Alonso A, Van der Elst W, et al. Statistical evaluation of surrogate endpoints with examples from cancer clinical trials. Biometrical J. 2016;58:104–32.

    Article  MathSciNet  MATH  Google Scholar 

  16. Buyse M, Sargent DJ, Grothey A, Matheson A, de Gramont A. Biomarkers and surrogate end points–the challenge of statistical validation. Nat Rev Clin Oncol. 2010;7:309–17.

    Article  Google Scholar 

  17. Committee on Strategies for Responsible Sharing of Clinical Trial Data. Discussion framework for clinical trial data sharing: guiding principles, elements, and activities. In: Institute of Medicine of the National Academies, editor. The Washington, DC: National Academies Press; 2014.

    Google Scholar 

  18. Daniels MJ, Hughes MD. Meta-analysis for the evaluation of potential surrogate markers. Stat Med. 1997;16:1965–82.

    Article  Google Scholar 

  19. Darby S, Davies C, McGale P. The early breast cancer trialists’ collaborative group: a brief history of results to date. In: Davison A, Dodge Y, Wermuth N, editors. Oxford: Oxford University Press; 2005.

    Google Scholar 

  20. Drazen JM. Sharing individual patient data from clinical trials. New Engl J Med. 2015;372:201–2.

    Article  Google Scholar 

  21. Frangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics. 2002;58:21–9.

    Article  MathSciNet  MATH  Google Scholar 

  22. Fredricks GA, Nelsen RB. On the relationship between Spearman’s rho and Kendall’s tau for pairs of continuous random variables. J Stat Plan Infer. 2007;137:2143–50.

    Article  MathSciNet  MATH  Google Scholar 

  23. Gail MH, Pfeiffer R, Van Houwelingen HC, Carroll RJ. On meta-analytic assessment of surrogate outcomes. Biostatistics. 2000;1:231–46.

    Article  MATH  Google Scholar 

  24. GASTRIC (Global Advanced/Adjuvant Stomach Tumor Research International Collaboration) Group, Oba K, Paoletti X, Bang Y-J, Bleiberg H, Burzykowski T, et al. Role of chemotherapy for advanced/recurrent gastric cancer: an individual-patient-data meta-analysis. Eur J Cancer. 2013;49:1565–77.

    Article  Google Scholar 

  25. GASTRIC (Global Advanced/Adjuvant Stomach Tumor Research International Collaboration) Group, Paoletti X, Oba K, Burzykowski T, Michiels S, Ohashi Y, et al. Benefit of adjuvant chemotherapy for resectable gastric cancer: a meta-analysis. JAMA. 2010;303:1729–3717.

    Article  Google Scholar 

  26. Krumholz HM, Peterson ED. Editorial: open access to clinical trials data. JAMA. 2014;312:11–2.

    Article  Google Scholar 

  27. Lassere MN. The Biomarker-Surrogacy Evaluation Schema: a review of the biomarker-surrogate literature and a proposal for a criterion-based, quantitative, multidimensional hierarchical levels of evidence schema for evaluating the status of biomarkers as surrogate endpoints. Stat Methods Med Res. 2008;17:303–40.

    Article  MathSciNet  Google Scholar 

  28. Mauguen A, Pignon J-P, Burdett S, Domerg C, Fisher D, Paulus R, et al. Surrogate endpoints for overall survival in chemotherapy and radiotherapy trials in operable and locally advanced lung cancer: a re-analysis of meta-analyses of individual patients’ data. Lancet Oncol. 2013;14:619–26.

    Article  Google Scholar 

  29. Michiels S, Le Maitre A, Buyse M, Burzykowski T, Maillard E, Bogaerts J, et al. Surrogate endpoints for overall survival in locally advanced head and neck cancer: meta-analyses of individual patient data. Lancet Oncol. 2009;10:341–50.

    Article  Google Scholar 

  30. Michiels S, Pugliano L, Marguet S, Grun D, Barinoff J, et al. Progression-free survival as surrogate endpoint for overall survival in clinical trials of HER2-targeted agents in HER2-positive metastatic breast cancer. Ann Oncol. 2016;27:1029–34.

    Article  Google Scholar 

  31. Oba K, Paoletti X, Alberts S, Bang Y-J, Benedetti J, Bleiberg H, et al. Disease-free survival as a surrogate for overall survival in adjuvant trials of gastric cancer: a meta-analysis. J Natl Cancer Inst. 2013;105:1600–7.

    Article  Google Scholar 

  32. Ocaña A, Amir E, Vera F, Eisenhauer EA, Tannock IF. Addition of bevacizumab to chemotherapy for treatment of solid tumors: similar results but different conclusions. J Clin Oncol. 2011;29:254–6.

    Article  Google Scholar 

  33. Paoletti X, Oba K, Bang Y, Bleiberg H, Boku N, Bouché O, et al. Progression-free survival as a surrogate for overall survival in advanced/recurrent gastric cancer trials: a meta-analysis. J Natl Cancer Inst. 2013; 1–4.

    Google Scholar 

  34. Prentice RL. Surrogate endpoints in clinical trials: definition and operational criteria. Stat Med. 1989;8:431–40.

    Article  Google Scholar 

  35. Riley RD, Lambert PC, Abo-zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ. 2010;340:c221.

    Article  Google Scholar 

  36. Sargent DJ, Wieand HS, Haller DG, Gray R, Benedetti JK, Buyse M, et al. Disease-free survival versus overall survival as a primary end point for adjuvant colon cancer studies: individual patient data from 20,898 patients on 18 randomized trials. J Clin Oncol. 2005;23:8664–70.

    Article  Google Scholar 

  37. Shi Q, de Gramont A, Grothey A, Zalcberg J, Chibaudel B, Schmoll H-J, et al. Individual patient data analysis of progression-free survival versus overall survival as a first-line end point for metastatic colorectal cancer in modern randomized trials: findings from the analysis and research in cancers of the digestive system databa. J Clin Oncol. 2015;33:22–8.

    Article  Google Scholar 

  38. Stewart LA, Clarke MJ. Practical methodology of meta-analyses (overviews) using updated individual patient data. Cochrane Working Group. Stat Med. 1995;14:2057–79.

    Article  Google Scholar 

  39. Stewart LA, Clarke M, Rovers M, Riley RD, Simmonds M, Stewart G, et al. Preferred reporting items for a systematic review and meta-analysis of individual participant data. JAMA. 2015;313:1657.

    Article  Google Scholar 

  40. Taichman DB, Backus J, Baethge C, Bauchner H, de Leeuw PW, Drazen JM, et al. Sharing clinical trial data: a proposal from the International Committee of Medical Journal Editors. PLOS Med. 2016;13:e1001950.

    Article  Google Scholar 

  41. Taylor JMG, Wang Y, Thiébaut R. Counterfactual links to the proportion of treatment effect explained by a surrogate marker. Biometrics. 2005;61:1102–11.

    Article  MathSciNet  Google Scholar 

  42. Vanderweele TJ, Tchetgen Tchetgen EJ. Mediation analysis with time varying exposures and mediators. J R Stat Soc B. 2016 (in press).

    Google Scholar 

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Author information

Authors and Affiliations

  1. Interfaculty Initiative in Information Studies, Graduate School of Interdisciplinary Information Studies & Department of Biostatistics, School of Public Health, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bukyo-ku, Tokyo, 113-0033, Japan

    Koji Oba

  2. Department of Biostatistics and Epidemiology, Gustave Roussy Cancer Center & INSERM U1018 CESP OncoStat, 114, avenue Edouard Vaillant, 94805, Villejuif Cedex, France

    Xavier Paoletti

Authors
  1. Koji Oba
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  2. Xavier Paoletti
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Correspondence to Koji Oba .

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Editors and Affiliations

  1. Graduate School of Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan

    Shigeyuki Matsui

  2. Cancer Research and Biostatistics, Seattle, Washington, USA

    John Crowley

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Oba, K., Paoletti, X. (2017). Evaluation of Surrogate Endpoints Using a Meta-Analysis Approach with Individual Patient Data: Summary of a Gastric Cancer Meta-Analysis Project. In: Matsui, S., Crowley, J. (eds) Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer, Singapore. https://doi.org/10.1007/978-981-10-0126-0_12

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