Skip to main content
SpringerLink
Log in
Book cover

Midwest Biopharmaceutical Statistics Workshop

MBSW 2016: Pharmaceutical Statistics pp 141–155Cite as

A Novel Framework for Bayesian Response-Adaptive Randomization

  • Conference paper
  • First Online:

  • 925 Accesses

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 218))

Abstract

The development of response-adaptive randomization (RAR) has taken many different paths over the past few decades. Some RAR schemes optimize certain criteria, but may be complicated and often rely on asymptotic arguments, which may not be suitable in trials with small sample sizes. Some Bayesian RAR schemes are very intuitive and easy to implement, but may not always be tailored toward the study goals. To bridge the gap between these methods, we proposed a framework in which easy-to-implement Bayesian RAR schemes can be derived to target the study goals. We showed that the popular Bayesian RAR scheme that assigns more patients to better performing arms fits in the new framework given a specific intention. We also illustrated the new framework in the setting where multiple treatment arms are compared to a concurrent control arm. Through simulation, we demonstrated that the RAR schemes developed under the new framework outperform a popular method in achieving the pre-specified study goals.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Barker, A.D., Sigman, C.C., Kelloff, G.J., Hylton, N.M., Berry, D.A., Esserman, L.J.: I-SPY 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clin. Pharmacolology Ther. 86(1), 97–100 (2009)

    Article  Google Scholar 

  2. Berry, D.A., Eick, S.G.: Adaptive assignment versus balanced randomization in clinical trials, a decision analysis. Stat. Med. 14, 231–246 (1995)

    Article  Google Scholar 

  3. Berry, D.A.: Adaptive clinical trials in oncology. Nat. Rev. Clin. Oncol. 9, 199–207 (2012)

    Article  Google Scholar 

  4. Bleck, T., Cock, H., Chamberlain, J., Cloyd, J., Connor, J., Elm, J., Fountain, N., Jones, E., Lowenstein, D., Shinnar, S., Silbergleit, R., Treiman, D., Trinka, E., Kapur, J.: The established status epilepticus trial 2013. Epilepsia 54, 89–92 (2013)

    Article  Google Scholar 

  5. Cheng, Y., Berry, D.A.: Optimal adaptive randomized designs for clinical trials. Biometrika 94, 673–689 (2007)

    Article  MathSciNet  Google Scholar 

  6. Collins, S.P., Lindsell, C.J., Pang, P.S., Storrow, A.B., Peacock, W.F., Levy, P., Rahbar, M.H., Del Junco, D., Gheorghiade, M., Berry, D.A: Bayesian adaptive trial design in acute heart failure syndromes: moving beyond the mega trial. Am. Hear. J. 164, 138-145 (2012)

    Article  Google Scholar 

  7. Connor, J.T., Luce, B.R., Broglio, K.R., Ishak, K.J., Mullins, C.D., Vanness, D.J., Fleurence, R., Saunders, E., Davis, B.R.: Do Bayesian adaptive trials offer advantages for comparative effectiveness research? Protocol for the RE-ADAPT study. Clin. Trials 10, 807–827 (2013)

    Article  Google Scholar 

  8. Eisele, J.: The doubly adaptive biased coin design for sequential clinical trials. J. Stat. Plan. Inference 38, 249–261 (1994)

    Article  MathSciNet  Google Scholar 

  9. Fiore, L.D., Brophy, M., Ferguson, R.E., D’Avolio, L., Hermos, J.A., Lewa, R.A., Doros, G., Conrada, C.H., O’Neil, J.A., Sabina, T.P., Kaufman, J., Swartz, S.L., Lawler, E., Lianga, M.H., Gaziano, M., Lavori, P.W.: A point-of-care clinical trial comparing insulin administered using a sliding scale versus a weight-based regimen. Clin. Trials 8, 183–195 (2011)

    Article  Google Scholar 

  10. Gallo, P., Chuang-Stein, C., Dragalin, V., Gaydos, B., Krams, M., Pinheiro, J.: PhRMA Working Group: adaptive designs in clinical drug development–an executive summary of the PhRMA Working Group. J. Biopharm. Stat. 16, 275–283 (2006)

    Article  Google Scholar 

  11. Genovese, M.C., Lee, E., Satterwhite, J., Veenhuizen, M., Disch, D., Berclaz, P.Y., Myers, S., Sides, G., Benichou, O.: A phase 2 dose-ranging study of subcutaneous tabalumab for the treatment of patients with active rheumatoid arthritis and an inadequate response to methotrexate. Ann. Rheum. Dis. 72(9), 1453–60 (2013)

    Article  Google Scholar 

  12. Hu, F., Rosenberger, W.F.: Optimality, variability, power: evaluating response-adaptive randomization procedures for treatment comparisons. J. Am. Stat. Assoc. 98, 671–678 (2003)

    Article  Google Scholar 

  13. Hu, F., Zhang, L.X.: Asymptotic properties of doubly adaptive biased coin designs for multi-treatment clinical trials. Ann. Stat. 32, 268–301 (2004)

    MATH  Google Scholar 

  14. Hu, F., Rosenberger, W.F.: The Theory of Response-adaptive Randomization in Clinical Trials. Wiley, Hoboken, NJ (2006)

    Book  Google Scholar 

  15. Kim, E.S., Herbst, R.S., Wistuba, I.I., Lee, J.J., Blumenschein Jr., G.R., Tsao, A., Stewart, D.J., Hicks, M.E., Erasmus Jr., J., Gupta, S., Alden, C.M., Liu, S., Tang, X., Khuri, F.R., Tran, H.T., Johnson, B.E., Heymach, J.V., Mao, L., Fossella, F., Kies, M.S., Papadimitrakopoulou, V., Davis, S.E., Lippman, S.M., Hong, W.K.: The BATTLE trial: personalizing therapy for lung cancer. Cancer Discov. 1, 44–53 (2011)

    Article  Google Scholar 

  16. Komaki, F., Biswas, A.: Bayesian optimal response-adaptive design for binary responses using stopping rule. Stat. Methods Med. Res. (2016). https://doi.org/10.1177/0962280216647210

    Article  Google Scholar 

  17. Krams, M., Lees, K.R., Hacke, W., Grieve, A.P., Orgogozo, J.M., Ford, G.A.: Acute stroke therapy by inhibition of neutrophils (ASTIN): an adaptive dose-response study of UK-279,276 in acute ischemic stroke. Stroke 34, 2543–2548 (2003)

    Article  Google Scholar 

  18. Lenz, R.A., Pritchett, Y.L., Berry, S.M., Llano, D.A., Han, S., Berry, D.A., Sadowsky, C.H., Abi-Saab, W.M., Saltarelli, M.D.: Adaptive, dose-finding phase 2 trial evaluating the safety and efficacy of ABT-089 in mild to moderate Alzheimer disease. Alzheimer Dis. Assoc. Disord. 29, 192–199 (2015)

    Article  Google Scholar 

  19. Lewis, R.J., Viele, K., Broglio, K., Berry, S.M., Jones, A.E.: An adaptive, phase II, dose-finding clinical trial design to evaluate L-carnitine in the treatment of septic shock based on efficacy and predictive probability of subsequent phase III success. Crit. Care Med. 41(7), 1674–1678 (2013)

    Article  Google Scholar 

  20. Lin, J., Bunn, V.: Comparison of multi-arm multi-stage design and adaptive randomization in platform clinical trials. Contemp. Clin. Trials 54, 48–59 (2017)

    Article  Google Scholar 

  21. Luce, B.R., Connor, J.T., Broglio, K.R., Mullins, C.D., Ishak, K.J., Saunders, E., Davis, B.R.: Using Bayesian adaptive trial designs for comparative effectiveness research: a virtual trial execution. Ann. Med. 165(6), 431–438 (2016)

    Google Scholar 

  22. Parmar, S., Andersson, B.S., Couriel, D., Munsell, M.F., Fernandez-Vina, M., Jones, R.B., Shpall, E.J., Popat, U., Anderlini, P., Giralt, S., Alousi, A., Cano, P., Bosque, D., Hosing, C., Silva Lde, P., Westmoreland, M., Wathen, J.K., Berry, D., Champlin, R.E., de Lima, M.J.: Prophylaxis of graft-versus-host disease in unrelated donor transplantation with pentostatin, tacrolimus, and mini-methotrexate: a phase I/II controlled, adaptively randomized study. J. Clin. Oncol. 29(3), 294–302 (2011)

    Article  Google Scholar 

  23. Popescu, I., Fleshner, P.R., Pezzullo, J.C., Charlton, P.A., Kosutic, G., Senagore, A.J.: The ghrelin agonist TZP-101 for management of postoperative ileus after partial colectomy: a randomized, dose-ranging, placebo-controlled clinical trial. Dis. Colon Rectum 53, 126–134 (2010)

    Article  Google Scholar 

  24. Rosenberger, W.F., Stallard, N., Ivanova, A., Harper, C.N., Ricks, M.L.: Optimal adaptive designs for binary response trials. Biometrics 57(3), 909–913 (2001)

    Article  MathSciNet  Google Scholar 

  25. Rosenberger, W.F., Sverdlov, O., Hu, F.: Adaptive randomization for clinical trials. J. Biopharm. Stat. 22(4), 719–736 (2012)

    Article  MathSciNet  Google Scholar 

  26. Skrivanek, Z., Gaydos, B.L., Chien, J.Y., Geiger, M.J., Heathman, M.A., Berry, S., Anderson, H., Forst, T., Milicevic, Z., Berry, D.: Dose-finding results in an adaptive, seamless, randomized trial of once-weekly dulaglutide combined with metformin in type 2 diabetes patients (AWARD-5). Diabetes Obes. Metab. 16, 748–756 (2014)

    Article  Google Scholar 

  27. Thall, P.F., Wathen, J.K.: Practical Bayesian adaptive randomization in clinical trials. Eur. J. Cancer 43, 859–866 (2007)

    Article  Google Scholar 

  28. Thall, P.F., Fox, P.S., Wathen, J.K.: Statistical controversies in clinical research: scientific and ethical problems with adaptive randomization in comparative clinical trials. Ann. Oncol. 26(8), 1621–1628 (2015)

    Article  Google Scholar 

  29. Thompson, W.R.: On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25, 275–294 (1933)

    Article  Google Scholar 

  30. Tymofyeyev, Y., Rosenberger, W.F., Hu, F.: Implementing optimal allocation in sequential binary response experiments. J. Am. Stat. Assoc. 102, 224–234 (2007)

    Article  MathSciNet  Google Scholar 

  31. Villar, S.S., Wason, J., Bowden, J.: Response-adaptive randomization for multi-arm clinical trials using the forward looking Gittins index rule. Biometrics 71, 969–978 (2015)

    Article  MathSciNet  Google Scholar 

  32. Wathen, J.K., Thall, P.F.: A simulation study of outcome adaptive randomization in multi-arm clinical trials. Clin. Trials 14(5), 432–440 (2017)

    Article  Google Scholar 

  33. Wei, L.J., Durham, S.: The randomized play-the-winner rule in medical trials. J. Am. Stat. Assoc. 73, 840–843 (1978)

    Article  Google Scholar 

  34. Yin, G., Chen, N., Lee, J.J.: Phase II trial design with Bayesian adaptive randomization and predictive probability. J. R. Stat. Soc. Ser. C 61(2), 219–235 (2012)

    Article  MathSciNet  Google Scholar 

  35. Zang, Y., Lee, J.J.: Adaptive clinical trial designs in oncology. Chin. Clin. Oncol. 3(4), 49 (2014)

    Google Scholar 

  36. Zelen, M.: Play the winner rule and the controlled clinical trial. J. Am. Stat. Assoc. 64, 131–146 (1969)

    Article  MathSciNet  Google Scholar 

  37. Zhu, H., Hu, F.: Sequential monitoring of response-adaptive randomized clinical trials. Ann. Stat. 38, 2218–2241 (2010)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Global Statistics, Takeda Pharmaceutical Company Limited, Tokyo, Japan

    Jian Zhu

  2. Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA

    Ina Jazić

  3. Takeda Pharmaceutical, Cambridge, MA, USA

    Yi Liu

Authors
  1. Jian Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ina Jazić
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jian Zhu .

Editor information

Editors and Affiliations

  1. Statistical Innovation and Consultation group, Takeda Pharmaceuticals, Cambridge, MA, USA

    Ray Liu

  2. Division of Biometrics VI, CDER, U.S. Food and Drug Administration , Silver Spring, MD, USA

    Yi Tsong

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhu, J., Jazić, I., Liu, Y. (2019). A Novel Framework for Bayesian Response-Adaptive Randomization. In: Liu, R., Tsong, Y. (eds) Pharmaceutical Statistics. MBSW 2016. Springer Proceedings in Mathematics & Statistics, vol 218. Springer, Cham. https://doi.org/10.1007/978-3-319-67386-8_11

Download citation

Publish with us

Policies and ethics

Search

Navigation