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Research Article

A Bayesian phase I–II clinical trial design to find the biological optimal dose on drug combination

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Pages 582-595 | Received 26 Oct 2022, Accepted 09 Jul 2023, Published online: 17 Jul 2023

References

  • Cai, C., Y. Yuan, and Y. Ji. 2014. A Bayesian dose finding design for oncology clinical trials of combinational biological agents. Journal of the Royal Statistical Society Series C, Applied Statistics 63 (1):159–173. doi:10.1111/rssc.12039.
  • Daud, A. I., K. Loo, M. L. Pauli, R. Sanchez-Rodriguez et al, 2016. Tumor immune profiling predicts response to anti–PD-1 therapy in human melanoma. Journal of Clinical Investigation 126 (9):3447–3452. JCI87324. doi:10.1172/JCI87324.
  • Gelman, A., A. Jakulin, M. G. Pittau, and Y.-S. Su. 2008. A weakly informative default prior distribution for logistic and other regression models. The Annals of Applied Statistics 2 (4). doi:10.1214/08-AOAS191.
  • Guo, W., Y. Ni, and Y. Ji. 2015. TEAMS: Toxicity- and Efficacy-based Dose insertion design with adaptive model selection for phase I/II Dose-escalation trials in oncology. Statistics in Biosciences 7 (2):432–459. doi:10.1007/s12561-015-9133-9.
  • Houede, N., P. F. Thall, H. Nguyen, X. Paoletti, and A. Kramar. 2010. Utility-based optimization of combination therapy using ordinal toxicity and efficacy in phase I/II trials. Biometrics 66 (2):532–540. doi:10.1111/j.1541-0420.2009.01302.x.
  • Jin, I., S. Liu, P. F. Thall, et al. 2014. Using Data Augmentation to Facilitate Conduct of Phase I-II Clinical Trials with Delayed Outcomes. Journal of the American Statistical Association 109 (506):525–536. doi:10.1080/01621459.2014.881740.
  • Kass, R. E., and A. E. Raftery. 1995. Bayes factors. Journal of the American Statistical Association 90 (430):773–795. doi:10.1080/01621459.1995.10476572.
  • Li, P., R. Liu, J. Lin, and Y. Ji. 2020. TEPI-2 and UBI: Designs for optimal immuno-oncology and cell therapy dose finding with toxicity and efficacy. Journal of Biopharmaceutical Statistics 30 (6):979–992. doi:10.1080/10543406.2020.1814802.
  • Lin, R., and G. Yin. 2017. STEIN: A simple toxicity and efficacy interval design for seamless phase I/II clinical trials. Statistics in Medicine 36 (26):4106–4120. doi:10.1002/sim.7428.
  • Lyu, J., Y. Ji, N. Zhao, and D. V. T. Catenacci. 2018. AAA: Triple adaptive Bayesian designs for the identification of optimal dose combinations in dual-agent dose finding trials. Journal of the Royal Statistical Society Series C, Applied Statistics 68 (2):385–410. doi:10.1111/rssc.12291.
  • Mozgunov, P., and T. Jaki. 2020. An information theoretic approach for selecting arms in clinical trials. Journal of the Royal Statistical Society Series B, Statistical Methodology 82 (5):1223–1247. doi:10.1111/rssb.12391.
  • O’Quigley, J., M. Pepe, and L. Fisher. 1990. Continual reassessment method: A practical design for phase I clinical trials in cancer. Biometrics 46 (1):33–48. doi:10.2307/2531628.
  • Riviere, M. K., Y. Yuan, F. Dubois, and S. Zohar. 2015. A Bayesian dose finding design for clinical trials combining a cytotoxic agent with a molecularly targeted agent. Journal of the Royal Statistical Society Series C, Applied Statistics 64 (1):215–229. doi:10.1111/rssc.12072.
  • Thall, P. F., H. Q. Nguyen, and R. G. Zinner. 2017. Parametric Dose Standardization for Optimizing Two-Agent Combinations in a Phase I–II Trial with Ordinal Outcomes. Journal of the Royal Statistical Society Series C, Applied Statistics 66 (1):201–224. doi:10.1111/rssc.12162.
  • Tighiouart, M. 2018. Two-Stage Design for Phase I–II Cancer Clinical Trials Using Continuous Dose Combinations of Cytotoxic Agents. Journal of the Royal Statistical Society Series C, Applied Statistics 68 (1):235–250. doi:10.1111/rssc.12294.
  • Wages, N. A., and C. Tait. 2015. Seamless phase I/II adaptive design for oncology trials of molecularly targeted agents. Journal of Biopharmaceutical Statistics 36 (5):643–660. doi:10.1080/10543406.2014.920873.
  • Wang, K., and A. Ivanova. 2005. Two-Dimensional dose finding in discrete dose space. Biometrics 61 (1):217–222. doi:10.1111/j.0006-341X.2005.030540.x.
  • Yada, S., and C. Hamada. 2018. A Bayesian hierarchal modeling approach to shortening phase I/II trials of anticancer drug combinations. Pharmaceutical Statistics 17 (6):750–760. doi:10.1002/pst.1895.
  • Yin, G., and Y. Yuan. 2009. Bayesian model averaging continual reassessment method in phase i clinical trials. Journal of the American Statistical Association 104 (487):954–968. doi:10.1198/jasa.2009.ap08425.
  • Yuan, Y., K. R. Hess, S. G. Hilsenbeck, and M. R. Gilbert. 2016. Bayesian optimal interval design: A simple and well-performing design for phase I oncology trials. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 22 (17):4291–4301. doi:10.1158/1078-0432.CCR-16-0592.
  • Yuan, Y., and G. Yin. 2011. Bayesian phase I/II adaptively randomized oncology trials with combined drugs. The Annals of Applied Statistics 5 (2A):924–942. doi:10.1214/10-AOAS433.
  • Zang, Y., and J. J. Lee. 2017. A robust two-stage design identifying the optimal biological dose for phase I/II clinical trials. Statistics in Medicine 36 (1):27–42. doi:10.1002/sim.7082.

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