On the Application of Flexible Designs When Searching for the Better of Two Anticancer Treatments

  • Christina Kunz
  • Lutz EdlerEmail author


In search for better treatment, biomedical researchers have defined an increasing number of new anticancer compounds attacking the tumour disease with drugs targeted to specific molecular structure and acting very differently from standard cytotoxic drugs. This has put high pressure on early clinical drug testing since drugs may need to be tested in parallel when only a limited number of patients—e.g., in rare diseases—or limited funding for a single compound is available. Furthermore, at planning stage, basic information to define an adequate design may be rudimentary. Therefore, flexibility in design and conduct of clinical studies has become one of the methodological challenges in the search for better anticancer treatments. Using the example of a comparative phase II study in patients with rare non-clear cell renal cell carcinoma and high uncertainty about effective treatment options, three flexible design options are explored for two-stage two-armed survival trials. Whereas the two considered classical group sequential approaches integrate early stopping for futility in two-sided hypothesis tests, the presented adaptive group sequential design enlarges these methods by sample size recalculation after the interim analysis if the study has not been stopped for futility. Simulation studies compare the characteristics of the different design approaches.


Clinical phase II trial Survival data Flexible design Sample size recalculation 


  1. 1.
    Bauer, P., Bretz, F., Dragalin, V., König, F., & Wassmer, G. (2016). Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls. Statistics in Medicine, 35, 325–347.MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bauer, P., & Köhne, K. (1994). Evaluation of experiments with adaptive interim analyses. Biometrics, 50, 1029–1041.CrossRefGoogle Scholar
  3. 3.
    Bauer, P., & Posch, M. (2004). Letter to the editor. Modification of the sample size and the schedule of interim analyses in survival trials based on data inspections, by H. Müller, H.-H. Schäfer, Statistics in Medicine 2001; 20:3741–3751. Statistics in Medicine, 23, 1333–1335.Google Scholar
  4. 4.
    Brannath, W., Posch, M., & Bauer, P. (2002). Recursive combination tests. Journal of the American Statistical Association, 97, 236–244.MathSciNetCrossRefGoogle Scholar
  5. 5.
    Chang, M. N., Hwang, I. K., & Shih, W. J. (1998). Group sequential designs using both type I and type II error probability spending functions. Communications in Statistics, Theory and Methods, A27(6), 1323–1339.Google Scholar
  6. 6.
    Cox, D. R. (1972). Regression models and life tables (with discussion). Journal of the Royal Statistical Society, B34, 187–220.zbMATHGoogle Scholar
  7. 7.
    Cox, D. R. (1975). Partial likelihood. Biometrika, 62(2), 269–276.Google Scholar
  8. 8.
    Denne, J. S. (2001). Sample size recalculation using conditional power. Statistics in Medicine, 20, 2645–2660.CrossRefGoogle Scholar
  9. 9.
    Desseaux, K., & Porcher, R. (2007). Flexible two-stage design with sample size reassessment for survival trials. Statistics in Medicine, 26(27), 5002–5013.Google Scholar
  10. 10.
    FDA. (2010). Adaptive design clinical trials for drugs and biologics – Draft guidance. U.S. Department of Health and Human Services, Food and Drug Administration.
  11. 11.
    FDA. (2016). Adaptive designs for medical device clinical studies – Guidance for industry and food and drug administration staff. U.S. Department of Health and Human Services, Food and Drug Administration.
  12. 12.
    Gu, M., & Ying, Z. (1995). Group sequential methods for survival data using partial likelihood score processes with covariate adjustment. Statistica Sinica, 5, 793–804.MathSciNetzbMATHGoogle Scholar
  13. 13.
    Hwang, I. K., Shih, W. J., & De Cani, J. S. (1990). Group sequential designs using a family of type I error probability spending functions. Statistics in Medicine, 9, 1439–1445.CrossRefGoogle Scholar
  14. 14.
    Irle, S., & Schäfer, H. (2012). Interim design modifications in time-to-event studies. Journal of the American Statistical Association, 107, 341–348.MathSciNetCrossRefGoogle Scholar
  15. 15.
    Jahn-Eimermacher, A., & Ingel, K. (2009). Adaptive trial design: A general methodology for censored time to event data. Contemporary Clinical Trials, 30, 171–177.CrossRefGoogle Scholar
  16. 16.
    Jenkins, M., Stone, A., & Jennison, C. (2011). An adaptive seamless phase II/III design for oncology trials with subpopulation selection using correlated survival endpoints. Pharmaceutical Statistics, 10(4), 347–356.Google Scholar
  17. 17.
    Jennison, C., & Turnbull, B. W. (2000). Group sequential methods with applications to clinical trials. London: Chapman and Hall/CRC.zbMATHGoogle Scholar
  18. 18.
    Lachin, J. M., & Foulkes, M. A. (1986). Evaluation of sample size and power for analyses of survival with allowance for nonuniform patient entry, losses to follow-up, noncompliance and stratification. Biometrics, 42, 507–519.CrossRefGoogle Scholar
  19. 19.
    Lan, K. K. G., & DeMets, D. L. (1983). Discrete sequential boundaries for clinical trials. Biometrika, 70(3), 659–663.Google Scholar
  20. 20.
    Lan, K. K. G., & DeMets, D. L. (1989). Group sequential procedures: Calendar versus information time. Statistics in Medicine, 8, 1191–1198.CrossRefGoogle Scholar
  21. 21.
    Lehmacher, W., & Wassmer, G. (1999). Adaptive sample size calculations in group sequential trials. Biometrics, 55, 1286–1290.CrossRefGoogle Scholar
  22. 22.
    Magirr, D., Jaki, T., Koenig, F., & Posch, M. (2014). Adaptive survival trials. Google Scholar
  23. 23.
    Mehta, C. R., & Pocock, S. J. (2011). Adaptive increase in sample size when interim results are promising: A practical guide with examples. Statistics in Medicine, 30(28), 3267–3284.Google Scholar
  24. 24.
    Müller, H. H., & Schäfer, H. (2004). A general statistical principle for changing a design any time during the course of a trial. Statistics in Medicine, 23, 2497–2508.CrossRefGoogle Scholar
  25. 25.
    Project team, R. The R Project for Statistical Computing.
  26. 26.
    Rubinstein, L. V., Gail, M. H., & Santner, T. J. (1981). Planning the duration of a comparative clinical trial with loss to follow-up and a period of continued observation. Journal of Chronic Diseases, 34, 469–479.CrossRefGoogle Scholar
  27. 27.
    Rudser, K. D., & Emerson, S. S. (2008). Implementing type I and type II error spending for two-sided group sequential designs. Contemporary Clinical Trials, 29, 351–358.CrossRefGoogle Scholar
  28. 28.
    Schäfer, H., & Müller, H. H. (2001). Modification of the sample size and the schedule of interim analyses in survival trials based on data inspections. Statistics in Medicine, 20, 3741–3751.CrossRefGoogle Scholar
  29. 29.
    Schmidinger, M., & Zielinski, C. C. (2009). Novel agents for renal cell carcinoma require novel selection paradigms to optimise first-line therapy. Cancer Treatment Reviews, 35, 289–296.CrossRefGoogle Scholar
  30. 30.
    Schmidt, R., Faldum, A., & Kwiecien, R. (2018). Adaptive designs of the one-sample logrank test. Biometrics, 74, 529–537. MathSciNetCrossRefGoogle Scholar
  31. 31.
    Schrader, A. J., Olbert, P. J., Hegele, A., Varga, Z., & Hofmann, R. (2008). Metastatic non-clear cell renal cell carcinoma: current therapeutic options. BJU International, 101, 1343–1345.CrossRefGoogle Scholar
  32. 32.
    Tsiatis, A. (1981). A large sample study of Cox’s regression model. The Annals of Statistics, 9(1), 93–108.Google Scholar
  33. 33.
    Tsiatis, A., Rosner, G. L., & Tritchler, D. L. (1985). Group sequential tests with censored survival data adjusting for covariates. Biometrika, 72(2), 365–373.Google Scholar
  34. 34.
    Tymofyeyev, Y. (2014). A review of available software and capabilities for adaptive designs. In W. He, J. Pinheiro, & O. M. Kuznetsova (Eds.), Practical considerations for adaptive trial design and implementation (pp. 139–155). New York: Springer.CrossRefGoogle Scholar
  35. 35.
    Wassmer, G. (2006). Planning and analyzing adaptive group sequential survival trials. Biometrical Journal, 48, 714–729.MathSciNetCrossRefGoogle Scholar
  36. 36.
    Wunder, C., Kopp-Schneider, A., & Edler, L. (2012). An adaptive group sequential phase II design to compare treatments for survival endpoints in rare patient entities. Journal of Biopharmaceutical Statistics, 22(2), 294–311.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.German Cancer Research CenterHeidelbergGermany

Personalised recommendations