Abstract

We propose a new two-/three-stage dose-finding design called Target Toxicity (TT) for phase I clinical trials, where we link the decision rules in the dose-finding process with the conclusions from a hypothesis test. The power to detect excessive toxicity is also given. This solves the problem of why the minimal number of patients is needed for the selected dose level. Our method provides a statistical explanation of traditional ‘3+3’ design using frequentist framework. The proposed method is very flexible and it incorporates other interval-based decision rules through different parameter settings. We provide the decision tables to guide investigators when to decrease, increase or repeat a dose for next cohort of subjects. Simulation experiments were conducted to compare the performance of the proposed method with other dose-finding designs. A free open source R package tsdf is available on CRAN. It is dedicated to deriving two-/three-stage design decision tables and perform dose-finding simulations.

References

1. Cheung, Y. K. (2013). dfcrm: Dose-finding by the continual reassessment method (Version, 0.2–2.1). [Google Scholar]
2. Cheung, Y. K., & Chappell, R. (2000). Sequential designs for phase I clinical trials with late-onset toxicities. Biometrics, 56(4), 1177–1182. https://doi.org/10.1111/j.0006-341X.2000.01177.x [Crossref], [Web of Science ®], [Google Scholar]
3. Goodman, S. N., Zahurak, M. L., & Piantadosi, S. (1995). Some practical improvements in the continual reassessment method for phase I studies. Statistics in Medicine, 14(11), 1149–1161. https://doi.org/10.1002/(ISSN)1097-0258 [Crossref], [Web of Science ®], [Google Scholar]
4. Guo, W., Wang, S.-J., Yang, S., Lynn, H., & Ji, Y. (2017). A Bayesian interval dose-finding design addressing Ockham's razor: mTPI-2. Contemporary Clinical Trials, 58, 23–33. https://doi.org/10.1016/j.cct.2017.04.006 [Crossref], [Web of Science ®], [Google Scholar]
5. Ivanova, A., Flournoy, N., & Chung, Y. (2007). Cumulative cohort design for dose-finding. Journal of Statistical Planning and Inference, 137(7), 2316–2327. https://doi.org/10.1016/j.jspi.2006.07.009 [Crossref], [Web of Science ®], [Google Scholar]
6. Jennison, C., & Turnbull, B. W. (2000). Group sequential methods with applications to clinical trials. Chapman and Hall. [Google Scholar]
7. Ji, Y., Li, Y., & Nebiyou Bekele, B. (2007). Dose-finding in phase I clinical trials based on toxicity probability intervals. Clinical Trials, 4(3), 235–244. https://doi.org/10.1177/1740774507079442 [Crossref], [Web of Science ®], [Google Scholar]
8. Ji, Y., Liu, P., Li, Y., & Nebiyou Bekele, B. (2010). A modified toxicity probability interval method for dose-finding trials. Clinical Trials, 7(6), 653–663. https://doi.org/10.1177/1740774510382799 [Crossref], [Web of Science ®], [Google Scholar]
9. Ji, Y., & Yang, S. (2017). On the interval-based dose-finding designs. arXiv:1706.03277. [Google Scholar]
10. Lee, S. M., & Y. K. Cheung (2009). Model calibration in the continual reassessment method. Clinical Trials, 6(3), 227–238. https://doi.org/10.1177/1740774509105076 [Crossref], [Web of Science ®], [Google Scholar]
11. Leung, D. H.-Y., & Wang, Y.-G. (2001). Isotonic designs for phase I trials. Controlled Clinical Trials, 22(2), 126–138. https://doi.org/10.1016/s0197-2456(00)00132-x [Crossref], [Google Scholar]
12. Liu, S., & Yuan, Y. (2015). Bayesian optimal interval designs for phase I clinical trials. Journal of the Royal Statistical Society: Series C (Applied Statistics), 64(3), 507–523. https://doi.org/10.1111/rssc.2015.64.issue-3 [Crossref], [Web of Science ®], [Google Scholar]
13. O'Brien, P. C., & Fleming, T. R. (1979). A multiple testing procedure for clinical trials. Biometrics, 35(3), 549–556. https://doi.org/10.2307/2530245 [Crossref], [Web of Science ®], [Google Scholar]
14. O'Quigley, J., Pepe, M., & Fisher, L. (1990). Continual reassessment method: A practical design for phase 1 clinical trials in cancer. Biometrics, 46(1), 33–48. https://doi.org/10.2307/2531628 [Crossref], [Web of Science ®], [Google Scholar]
15. Paoletti, X., O'Quigley, J., & Maccario, J. (2004). Design efficiency in dose finding studies. Computational Statistics & Data Analysis, 45(2), 197–214. https://doi.org/10.1016/S0167-9473(02)00323-7 [Crossref], [Web of Science ®], [Google Scholar]
16. Reiner, E., Paoletti, X., & O'Quigley, J. (1999). Operating characteristics of the standard phase i clinical trial design. Computational Statistics & Data Analysis, 30(3), 303–315. https://doi.org/10.1016/S0167-9473(98)00095-4 [Crossref], [Web of Science ®], [Google Scholar]
17. Storer, B. E. (2001). An evaluation of phase I clinical trial designs in the continuous dose-response setting. Statistics in Medicine, 20(16), 2399–2408. https://doi.org/10.1002/(ISSN)1097-0258 [Crossref], [Web of Science ®], [Google Scholar]
18. Yuan, Y., Hess, K. R., Hilsenbeck, S. G., & Gilbert, M. R. (2016). Bayesian optimal interval design: A simple and well-performing design for phase I oncology trials. Clinical Cancer Research, 22(17), 4291–4301. https://doi.org/10.1158/1078-0432.CCR-16-0592 [Crossref], [Web of Science ®], [Google Scholar]