Skip to main content
Springer Nature Link
Log in

Biomarker-Defined Subgroup Selection Adaptive Design for Phase III Confirmatory Trial with Time-to-Event Data: Comparing Group Sequential and Various Adaptive Enrichment Designs

  • Published:
Statistics in Biosciences Aims and scope Submit manuscript

Abstract

Predictive and prognostic biomarkers play an important role in personalized medicine to determine strategies for drug evaluation and treatment selection. In the context of continuous biomarkers, identification of an optimal cutoff for patient selection can be challenging due to limited information on biomarker predictive value, the biomarker’s distribution in the intended use population, and the complexity of the biomarker relationship to clinical outcomes. As a result, prespecified candidate cutoffs may be rationalized based on biological and practical considerations. In this context, adaptive enrichment designs have been proposed with interim decision rules to select a biomarker-defined subpopulation to optimize study performance. With a group sequential design as a reference, the performance of several proposed adaptive designs are evaluated and compared under various scenarios (e.g., sample size, study power, enrichment effects) where type I error rates are well controlled through closed testing procedures and where subpopulation selections are based upon the predictive probability of trial success. It is found that when the treatment is more effective in a subpopulation, these adaptive designs can improve study power substantially. Furthermore, we identified one adaptive design to have generally higher study power than the other designs under various scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Simon RM (2013) Genomic clinical trials and predictive medicine. Cambridge University Press, Cambridge

    Book  Google Scholar 

  2. Mandrekar SJ, Sargent DJ (2009) Clinical trial designs for predictive biomarker validation: theoretical considerations and practical challenges. J Clin Oncol 27(24):4027–4034

    Article  Google Scholar 

  3. Simon R, Maitournam A (2004) Evaluating the efficiency of targeted designs for randomized clinical trials. Clin Cancer Res 10(20):6759–6763

    Article  Google Scholar 

  4. Maitournam A, Simon R (2005) On the efficiency of targeted clinical trials. Stat Med 24(3):329–339

    Article  MathSciNet  Google Scholar 

  5. Scher HI et al (2011) Adaptive clinical trial designs for simultaneous testing of matched diagnostics and therapeutics. Clin Cancer Res 17(21):6634–6640

    Article  Google Scholar 

  6. Sikorski R, Yao B (2009) Parallel paths to predictive biomarkers in oncology: uncoupling of emergent biomarker development and phase III trial execution. Sci Transl Med. doi:10.1126/scitranslmed.3000287

  7. Wang S-J, James Hung HM, O’Neill RT (2009) Adaptive patient enrichment designs in therapeutic trials. Biometr J 51(2):358–374

    Article  MathSciNet  Google Scholar 

  8. Mehta C et al (2009) Optimizing trial design: sequential, adaptive, and enrichment strategies. Circulation 119(4):597–605

    Article  Google Scholar 

  9. Song JX (2014) A two-stage patient enrichment adaptive design in phase II oncology trials. Contempor Clin Trials 37(1):148–154

    Article  Google Scholar 

  10. Proschan MA, Waclawiw MA (2000) Practical guidelines for multiplicity adjustment in clinical trials. Control Clin Trials 21(6):527–539

    Article  Google Scholar 

  11. Alosh M, Huque MF (2009) A flexible strategy for testing subgroups and overall population. Stat Med 28(1):3–23

    Article  MathSciNet  Google Scholar 

  12. Kim ES et al (2011) The BATTLE trial: personalizing therapy for lung cancer. Cancer Discov 1(1):44–53

    Article  Google Scholar 

  13. Liu A et al (2010) A threshold sample-enrichment approach in a clinical trial with heterogeneous subpopulations. Clin Trials 7(5):537–545

    Article  Google Scholar 

  14. Rosenblum M, van der Laan MJ (2011) Optimizing randomized trial designs to distinguish which subpopulations benefit from treatment. Biometrika 98(4):845–860

    Article  MathSciNet  MATH  Google Scholar 

  15. Wang S-J, O’Neill RT, Hung HMJ (2007) Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharm Stat 6(3):227–244

    Article  Google Scholar 

  16. Simon N, Simon R (2013) Adaptive enrichment designs for clinical trials. Biostatistics 14(4):613–625

    Article  Google Scholar 

  17. Brannath W et al (2009) Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology. Stat Med 28(10):1445–1463

    Article  MathSciNet  Google Scholar 

  18. Bretz F et al (2006) Confirmatory seamless phase II/III clinical trials with hypotheses selection at interim: general concepts. Biometr J 48(4):623–634

    Article  MathSciNet  Google Scholar 

  19. Hommel G (2001) Adaptive modifications of hypotheses after an interim analysis. Biometr J 43(5):581–589

    Article  MathSciNet  MATH  Google Scholar 

  20. Bauer P, Kieser M (1999) Combining different phases in the development of medical treatments within a single trial. Stat Med 18(14):1833–1848

    Article  Google Scholar 

  21. Kieser M, Bauer P, Lehmacher W (1999) Inference on multiple endpoints in clinical trials with adaptive interim analyses. Biometr J 41(3):261–277

    Article  MATH  Google Scholar 

  22. Schäfer H, Müller H-H (2001) Modification of the sample size and the schedule of interim analyses in survival trials based on data inspections. Stat Med 20(24):3741–3751

    Article  Google Scholar 

  23. Wassmer G (2006) Planning and analyzing adaptive group sequential survival trials. Biometr J 48(4):714–729

    Article  MathSciNet  Google Scholar 

  24. Shen Y, Cai J (2003) Sample size reestimation for clinical trials with censored survival data. J Am Stat Assoc 98(462):418–426

    Article  MathSciNet  MATH  Google Scholar 

  25. Simes RJ (1986) An improved Bonferroni procedure for multiple tests of significance. Biometrika 73(3):751–754

    Article  MathSciNet  MATH  Google Scholar 

  26. Lehmacher W, Wassmer G (1999) Adaptive sample size calculations in group sequential trials. Biometrics 55(4):1286–1290

    Article  MATH  Google Scholar 

  27. Bauer P, Kohne K (1994) Evaluation of experiments with adaptive interim analyses. Biometrics 50(4):1029–1041

    Article  MATH  Google Scholar 

  28. Brannath W, Posch M, Bauer P (2002) Recursive combination tests. J Am Stat Assoc 97(457):236–244

    Article  MathSciNet  MATH  Google Scholar 

  29. Proschan MA, Hunsberger SA (1995) Designed extension of studies based on conditional power. Biometrics 51(4):1315–1324

    Article  MATH  Google Scholar 

  30. O’Brien PC, Fleming TR (1979) A multiple testing procedure for clinical trials. Biometrics 35(3):549–556

    Article  Google Scholar 

  31. Hothorn T, Zeileis A (2008) Generalized maximally selected statistics. Biometrics 64(4):1263–1269

    Article  MathSciNet  MATH  Google Scholar 

  32. Faraggi D, Simon R (1996) A simulation study of cross-validation for selecting an optimal cutpoint in univariate survival analysis. Stat Med 15(20):2203–2213

    Article  Google Scholar 

  33. Williams, B. et al. (2006) Finding optimal cutpoints for continuous covariates with binary and time-to-event outcomes. http://www.mayo.edu/research/documents/biostat-79pdf/doc-10027230

  34. Su M, Fang L, Su Z (2013) A likelihood and resampling based approach to dichotomizing a continuous biomarker in medical research. J Biopharm Stat 23(3):637–647

    Article  MathSciNet  Google Scholar 

  35. Zucker DM, Agami S, Spiegelman D (2013) Testing for a changepoint in the cox survival regression model. J Stat Theory Pract 7(2):360–380

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Vertex Pharmaceuticals Incorporated, 50 Northern Ave, Boston, MA, USA

    Rui Tang

  2. Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA, USA

    Xiaoye Ma, Hui Yang & Michael Wolf

Authors
  1. Rui Tang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xiaoye Ma
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Hui Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Michael Wolf
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Rui Tang.

Appendix: Simulation Setup and Results

Appendix: Simulation Setup and Results

See Tables 1, 2, 3, 4, 5, 6, and 7.

Table 1 Simulation scenarios
Full size table
Table 2 Simulation results assuming uniform biomarker
Full size table
Table 3 Simulation results assuming normal biomarker
Full size table
Table 4 Simulation results assuming left-skewed biomarker
Full size table
Table 5 Simulation results assuming right-skewed biomarker
Full size table
Table 6 Simulation results assuming bimodal biomarker
Full size table
Table 7 Optimal adaptive design (AD) under all scenarios
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, R., Ma, X., Yang, H. et al. Biomarker-Defined Subgroup Selection Adaptive Design for Phase III Confirmatory Trial with Time-to-Event Data: Comparing Group Sequential and Various Adaptive Enrichment Designs. Stat Biosci 10, 371–404 (2018). https://doi.org/10.1007/s12561-017-9198-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12561-017-9198-8

Keywords