Adaptive Seamless Dose-Finding Trials
Published Online:13 Jun 2024https://doi.org/10.1287/msom.2023.0246
References
- (2002) Using confidence bounds for exploitation-exploration trade-offs. J. Machine Learn. Res. 3(November):397–422.Google Scholar
- Auer P, Ortner R, Szepesvári C (2007) Improved rates for the stochastic continuum-armed bandit problem. Bshouty NH, Gentile C, eds. Learning Theory. COLT 2007, Lecture Notes in Computer Science, vol. 4539 (Springer, Berlin, Heidelberg), 454–468.Google Scholar
- (2021) On multi-armed bandit designs for phase I clinical trials. J. Machine Learn. Res. 22(14):1–38.Google Scholar
- (2014) Development of the National Cancer Institute’s patient-reported outcomes version of the common terminology criteria for adverse events (PRO-CTCAE). J. National Cancer Inst. 106(9):dju244.Crossref, Google Scholar
- (2020) Online decision making with high-dimensional covariates. Oper. Res. 68(1):276–294.Link, Google Scholar
- (2016) An analytics approach to designing combination chemotherapy regimens for cancer. Management Sci. 62(5):1511–1531.Link, Google Scholar
- (2017) The real-world ethics of adaptive-design clinical trials. Hastings Center Rep. 47(6):27–37.Crossref, Google Scholar
- (2002) The bivariate continual reassessment method: Extending the CRM to phase I trials of two competing outcomes. Controlled Clinical Trials 23(3):240–256.Crossref, Google Scholar
- (2019) Dynamic pricing in an evolving and unknown marketplace. Preprint, submitted May 5, http://dx.doi.org/10.2139/ssrn.3382957.Google Scholar
- (2014) Combinatorial pure exploration of multi-armed bandits. Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger KQ, eds. Adv. Neural Inform. Processing Systems, vol. 27 (Curran Associates Inc., Red Hook, NY), 379–387.Google Scholar
- (2022) Bayesian sequential learning for clinical trials of multiple correlated medical interventions. Management Sci. 68(7):4919–4938.Link, Google Scholar
- ClinicalTrials.gov (2022) Study of peposertib in combination with capecitabine and RT in rectal cancer. Accessed March 2023, https://clinicaltrials.gov/ct2/show/results/NCT03770689?rslt=With&type=Intr&phase=01&draw=4&view=results.Google Scholar
- (2020) Unimodal bandits with continuous arms: Order-optimal regret without smoothness. Proc. ACM Measurement Anal. Comput. Systems 4(1):1–28.Crossref, Google Scholar
- (2015) Early phase clinical trials to identify optimal dosing and safety. Molecular Oncology 9(5):997–1007.Crossref, Google Scholar
- (2020) Discontinuous demand functions: Estimation and pricing. Management Sci. 66(10):4516–4534.Link, Google Scholar
- (2008) Adaptive designs for selecting drug combinations based on efficacy–toxicity response. J. Statist. Planning Inference 138(2):352–373.Crossref, Google Scholar
- (2013) Designing multi-objective multi-armed bandits algorithms: A study. 2013 Internat. Joint Conf. Neural Networks (IJCNN) (IEEE, Piscataway, NJ), 1–8.Google Scholar
- (2017) Thresholding bandit for dose-ranging: The impact of monotonicity. Preprint, submitted November 13, https://arxiv.org/abs/1711.04454v1.Google Scholar
- (2019) Bootstrapping upper confidence bound. Wallach H, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox E, Garnett R, eds. Adv. Neural Inform. Processing Systems, vol. 32 (Curran Associates, Inc., Red Hook, NY), 12123–12133.Google Scholar
- (2005) Risks and benefits of phase 1 oncology trials, 1991 through 2002. New England J. Medicine 352(9):895–904.Crossref, Google Scholar
- (2010) Utility-based optimization of combination therapy using ordinal toxicity and efficacy in phase I/II trials. Biometrics 66(2):532–540.Crossref, Google Scholar
- (2015) Sample sizes in dosage investigational clinical trials: A systematic evaluation. Drug Design Development Therapy 9:305–312.Crossref, Google Scholar
- (2010) Approaches to phase 1 clinical trial design focused on safety, efficiency, and selected patient populations: A report from the Clinical Trial Design Task Force of the National Cancer Institute Investigational Drug Steering Committee. Clinical Cancer Res. 16(6):1726–1736.Crossref, Google Scholar
- (2010) Phase I oncology studies: Evidence that in the era of targeted therapies patients on lower doses do not fare worse. Clinical Cancer Res. 16(4):1289–1297.Crossref, Google Scholar
- (2013) Online learning under delayed feedback. Sanjoy D, McAllester D, eds. Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 28 (PMLR, New York), 1453–1461.Google Scholar
- (2024) Contextual learning with online convex optimization: Theory and application to chronic diseases. Management Sci. Forthcoming.Google Scholar
- (2005) Nearly tight bounds for the continuum-armed bandit problem. Saul L, Weiss Y, Bottou L, eds. Adv. Neural Inform. Processing Systems, vol. 17 (MIT Press, Cambridge, MA), 697–704.Google Scholar
- (2018) Sotatercept with long-term extension for the treatment of anaemia in patients with lower-risk myelodysplastic syndromes: A phase 2, dose-ranging trial. Lancet Haematol. 5(2):e63–e72.Crossref, Google Scholar
- (1994) A comparison of two phase I trial designs. Statist. Medicine 13(18):1799–1806.Crossref, Google Scholar
- (2005) Risks and benefits of phase 1 oncology trials, revisited. New England J. Medicine 352(9):930–932.Crossref, Google Scholar
- (2021) Moving beyond 3+ 3: The future of clinical trial design. Amer. Soc. Clinical Oncology Ed. Book 41:e133–e144.Crossref, Google Scholar
- (2009) Dose escalation methods in phase I cancer clinical trials. J. National Cancer Inst. 101(10):708–720.Crossref, Google Scholar
- (2017) STEIN: A simple toxicity and efficacy interval design for seamless phase I/II clinical trials. Statist. Medicine 36(26):4106–4120.Crossref, Google Scholar
- (2016) A robust Bayesian dose-finding design for phase I/II clinical trials. Biostatistics 17(2):249–263.Crossref, Google Scholar
- (2016) An optimal algorithm for the thresholding bandit problem. Balcan MF, Weinberger KQ, eds. Proc. 33rd Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 48 ( JMLR.org), 1690–1698.Google Scholar
- (2007) An adaptive phase I design for identifying a biologically optimal dose for dual agent drug combinations. Statist. Medicine 26(11):2317–2330.Crossref, Google Scholar
- (2005) A Bayesian approach to jointly modeling toxicity and biomarker expression in a phase I/II dose-finding trial. Biometrics 61(2):343–354.Crossref, Google Scholar
- (1990) Continual reassessment method: A practical design for phase 1 clinical trials in cancer. Biometrics 46(1):33–48.Crossref, Google Scholar
- (2016) Phase III trial failures: Costly, but preventable. Appl. Clinical Trials 25(8).Google Scholar
- (2018) Phase I/II dose-finding design for molecularly targeted agent: Plateau determination using adaptive randomization. Statist. Methods Medical Res. 27(2):466–479.Crossref, Google Scholar
- (2007) Translation of innovative designs into phase I trials. J. Clinical Oncology 25(31):4982–4986.Crossref, Google Scholar
- (2020) A multicenter phase Ib/II study of DNA-PK inhibitor peposertib (M3814) in combination with capecitabine and radiotherapy in patients with locally advanced rectal cancer. J. Clinical Oncology 38(15 suppl).Crossref, Google Scholar
- (2014) Scientific and regulatory reasons for delay and denial of FDA approval of initial applications for new drugs, 2000–2012. J. Amer. Medical Assoc. 311(4):378–384.Crossref, Google Scholar
- (2014) Personalized dose selection in radiation therapy using statistical models for toxicity and efficacy with dose and biomarkers as covariates. Statist. Medicine 33(30):5330–5339.Crossref, Google Scholar
- (2019) Introduction to multi-armed bandits. Foundations Trends Machine Learn. 12(1–2):1–286.Crossref, Google Scholar
- (2006) Assessment of futility in clinical trials. Pharmaceutical Statist. 5(4):273–281.Crossref, Google Scholar
- (1989) Design and analysis of phase I clinical trials. Biometrics 45(3):925–937.Crossref, Google Scholar
- (2015) Dose finding method in joint modeling of efficacy and safety endpoints in phase II studies. Internat. J. Statist. Probab. 4(1):33–45.Google Scholar
- (2004) Dose-finding based on efficacy–toxicity trade-offs. Biometrics 60(3):684–693.Crossref, Google Scholar
- (2022) Adaptive learning of drug quality and optimization of patient recruitment for clinical trials with dropouts. Manufacturing Service Oper. Management 24(1):580–599.Link, Google Scholar
- (2015) Seamless phase I/II adaptive design for oncology trials of molecularly targeted agents. J. Biopharmaceutical Statist. 25(5):903–920.Crossref, Google Scholar
- (2017) Bayesian adaptive dose-escalation designs for simultaneously estimating the optimal and maximum safe dose based on safety and efficacy. Pharmaceutical Statist. 16(6):396–413.Crossref, Google Scholar
- (2012) Clinical Trial Design: Bayesian and Frequentist Adaptive Methods (John Wiley & Sons, Hoboken, NJ).Google Scholar
- (2006) Bayesian dose-finding in phase I/II clinical trials using toxicity and efficacy odds ratios. Biometrics 62(3):777–787.Crossref, Google Scholar
- (2006) An adaptive dose-finding design incorporating both toxicity and efficacy. Statist. Medicine 25(14):2365–2383.Crossref, Google Scholar

