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Lifetime Data Analysis

, Volume 17, Issue 1, pp 156–174 | Cite as

Bayesian phase II adaptive randomization by jointly modeling time-to-event efficacy and binary toxicity

  • Xiudong Lei
  • Ying Yuan
  • Guosheng Yin
Article

Abstract

In oncology, toxicity is typically observable shortly after a chemotherapy treatment, whereas efficacy, often characterized by tumor shrinkage, is observable after a relatively long period of time. In a phase II clinical trial design, we propose a Bayesian adaptive randomization procedure that accounts for both efficacy and toxicity outcomes. We model efficacy as a time-to-event endpoint and toxicity as a binary endpoint, sharing common random effects in order to induce dependence between the bivariate outcomes. More generally, we allow the randomization probability to depend on patients’ specific covariates, such as prognostic factors. Early stopping boundaries are constructed for toxicity and futility, and a superior treatment arm is recommended at the end of the trial. Following the setup of a recent renal cancer clinical trial at M. D. Anderson Cancer Center, we conduct extensive simulation studies under various scenarios to investigate the performance of the proposed method, and compare it with available Bayesian adaptive randomization procedures.

Keywords

Adaptive randomization Efficacy Phase II trial Survival analysis Time-to-event endpoint Toxicity 

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References

  1. Albert JH, Chib S (1993) Bayesian analysis of binary and polychotomous response data. J Am Stat Assoc 88: 669–679zbMATHCrossRefMathSciNetGoogle Scholar
  2. Berry DA, Eick SG (1995) Adaptive assignment versus balanced randomization in clinical trials: a decision analysis. Stat Med 14: 231–246CrossRefGoogle Scholar
  3. Chang MN, Therneau TM, Wieand HS, Cha SS (1987) Designs for group sequential phase II clinical trials. Biometrics 43: 865–874zbMATHCrossRefGoogle Scholar
  4. Conaway MR, Petroni GR (1996) Designs for phase II trials allowing for a trade-off between response and toxicity. Biometrics 52: 1375–1386zbMATHCrossRefMathSciNetGoogle Scholar
  5. Cox DR (1972) Regression models and life-tables (with discussion). J R Stat Soc Ser B 34: 187–220zbMATHGoogle Scholar
  6. Eick SG (1988) The two-armed bandit with delayed responses. Ann Stat 16: 254–264zbMATHCrossRefMathSciNetGoogle Scholar
  7. Eisele J (1994) The doubly adaptive biased coin design for sequential clinical trials. J Stat Plan Inference 38: 249–262zbMATHCrossRefMathSciNetGoogle Scholar
  8. Fleming TR (1982) One-sample multiple testing procedure for phase II clinical trials. Biometrics 38: 143–151zbMATHCrossRefGoogle Scholar
  9. Gehan EA (1961) The determination of the number of patients required in a preliminary and a follow-up trial of a new chemotherapeutic agent. J Chronic Dis 13: 346–353CrossRefGoogle Scholar
  10. Hu F, Rosenberger WF (2006) The theory of response-adaptive randomization in clinical trials. Wiley, New YorkzbMATHCrossRefGoogle Scholar
  11. Louis TA (1977) Sequential allocation in clinical trials comparing two exponential survival curves. Biometrics 33: 627–634CrossRefMathSciNetGoogle Scholar
  12. Ratain MJ, Sargent DJ (2009) Optimising the design of phase II oncology trials: the importance of randomisation. Eur J Cancer 45: 275–280CrossRefGoogle Scholar
  13. Rosenberger WF (1996) New directions in adaptive designs. Stat Sci 11: 137–149CrossRefGoogle Scholar
  14. Simon R (1989) Optimal two-stage designs for phase II clinical trials. Controll Clin Trials 10: 1–10CrossRefGoogle Scholar
  15. Thall PF, Cook JD (2004) Dose-finding based on efficacy–toxicity trade-offs. Biometrics 60: 684–693zbMATHCrossRefMathSciNetGoogle Scholar
  16. Thall PF, Wathen JK (2007) Practical Bayesian adaptive randomisation in clinical trials. Eur J Cancer 43: 859–866CrossRefGoogle Scholar
  17. Wei LJ, Durham S (1978) The randomized play-the-winner rule in medical trials. J Am Stat Assoc 73: 840–843zbMATHCrossRefGoogle Scholar
  18. Yin G, Li Y, Ji Y (2006) Bayesian dose-finding in phase I/II clinical trials using toxicity and efficacy odds ratios. Biometrics 62: 777–787zbMATHCrossRefMathSciNetGoogle Scholar
  19. Yuan Y, Yin G (2009) Bayesian dose finding by jointly modeling toxicity and efficacy as time-to-event outcomes. J R Stat Soc Ser C 58: 719–736CrossRefGoogle Scholar
  20. Zelen M (1969) Play the winner rule and the controlled clinical trial. J Am Stat Assoc 64: 131–146CrossRefMathSciNetGoogle Scholar
  21. Zhang L, Rosenberger WF (2007) Response-adaptive randomization for survival trials: the parametric approach. Appl Stat 56: 153–165zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Biostatistics, Unit 1411The University of Texas M. D. Anderson Cancer CenterHoustonUSA
  2. 2.Department of Statistics and Actuarial ScienceUniversity of Hong KongHong KongChina

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