L. Ellis, Antiangiogenic Therapy: More Promise and, Yet Again, More Questions, Journal of Clinical Oncology, vol.21, issue.21
DOI : 10.1200/JCO.2003.07.977

B. Morgan, A. Thomas, J. Drevs, J. Hennig, M. Buchert et al., Dynamic Contrast-Enhanced Magnetic Resonance Imaging As a Biomarker for the Pharmacological Response of PTK787/ZK 222584, an Inhibitor of the Vascular Endothelial Growth Factor Receptor Tyrosine Kinases, in Patients With Advanced Colorectal Cancer and Liver Metastases: Results From Two Phase I Studies, Journal of Clinical Oncology, vol.21, issue.21, pp.3955-3964, 2003.
DOI : 10.1200/JCO.2003.08.092

S. Hunsberger, L. Rubinstein, J. Dancey, and E. Korn, Dose escalation trial designs based on a molecularly targeted endpoint, Statistics in Medicine, vol.96, issue.14, pp.2171-2181, 2005.
DOI : 10.1002/sim.2102

A. Hirakawa, An adaptive dose-finding approach for correlated bivariate binary and continuous outcomes in phase I oncology trials, Statistics in Medicine, vol.61, issue.6, pp.516-532, 2012.
DOI : 10.1002/sim.4425

C. Cai, Y. Yuan, and J. Y. , A Bayesian Phase I/II Design for Oncology Clinical Trials of Combining Biological Agents, Journal of the Royal Statistical Society: Series C, 2013.

A. Ivanova and C. Xiao, Dose finding when the target dose is on a plateau of a dose-response curve: comparison of fully sequential designs, Pharmaceutical Statistics, vol.136, issue.5, pp.309-314, 2013.
DOI : 10.1002/pst.1585

W. Zhang, D. Sargent, and S. Mandrekar, An adaptive dose-finding design incorporating both toxicity and efficacy, Statistics in Medicine, vol.52, issue.14, pp.2365-2383, 2006.
DOI : 10.1002/sim.2325

J. Stiles, C. Amaya, S. Rains, D. Diaz, R. Pham et al., Targeting of Beta Adrenergic Receptors Results in Therapeutic Efficacy against Models of Hemangioendothelioma and Angiosarcoma, PLoS ONE, vol.39, issue.3, p.60021, 2013.
DOI : 10.1371/journal.pone.0060021.t002

S. Liu, G. Yin, and Y. Yuan, Bayesian data augmentation dose finding with continual reassessment method and delayed toxicity, The Annals of Applied Statistics, vol.7, issue.4, pp.2138-2156, 2013.
DOI : 10.1214/13-AOAS661SUPP

Y. Yuan and G. Yin, Bayesian phase I/II adaptively randomized oncology trials with combined drugs, The Annals of Applied Statistics, vol.5, issue.2A, pp.924-942, 2011.
DOI : 10.1214/10-AOAS433

I. Jin, S. Liu, P. Thall, and Y. Yuan, Using Data Augmentation to Facilitate Conduct of Phase I???II Clinical Trials With Delayed Outcomes, Journal of the American Statistical Association, vol.51, issue.506, pp.525-536, 2014.
DOI : 10.1002/sim.2325

M. Riviere, Y. Yuan, F. Dubois, and S. Zohar, 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), vol.10, issue.1, pp.215-229, 2014.
DOI : 10.1111/rssc.12072
URL : https://hal.archives-ouvertes.fr/hal-01298649

O. Quigley, J. Pepe, M. Fisher, and L. , Continual Reassessment Method: A Practical Design for Phase 1 Clinical Trials in Cancer, Biometrics, vol.46, issue.1, pp.33-48, 1990.
DOI : 10.2307/2531628

S. Chevret, The continual reassessment method in cancer phase i clinical trials: A simulation study, Statistics in Medicine, vol.43, issue.12, pp.1093-1108, 1993.
DOI : 10.1002/sim.4780121201

S. Liu and . Ning-j, A Bayesian Dose-finding Design for Drug Combination Trials with Delayed Toxicities, Bayesian Analysis, vol.8, issue.3, pp.703-722, 2013.
DOI : 10.1214/13-BA839

Y. Yuan and G. Yin, Robust EM Continual Reassessment Method in Oncology Dose Finding, Journal of the American Statistical Association, vol.106, issue.495, pp.818-831, 2011.
DOI : 10.1198/jasa.2011.ap09476

Y. Cheung and R. Chappell, Sequential Designs for Phase I Clinical Trials with Late-Onset Toxicities, Biometrics, vol.14, issue.Suppl., pp.1177-1182, 2000.
DOI : 10.1111/j.0006-341X.2000.01177.x

Y. Cheung and P. Thall, Monitoring the Rates of Composite Events with Censored Data in Phase II Clinical Trials, Biometrics, vol.72, issue.1, pp.89-97, 2002.
DOI : 10.1111/j.0006-341X.2002.00089.x

W. Thompson, ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES, Biometrika, vol.25, issue.3-4, pp.285-294, 1933.
DOI : 10.1093/biomet/25.3-4.285

H. Robbins, Some aspects of the sequential design of experiments, Bulletin of the American Mathematical Society, vol.58, issue.5, pp.527-535, 1952.
DOI : 10.1090/S0002-9904-1952-09620-8

J. Gittins, Bandit processes and dynamic allocation indices, JRSS, Series B, vol.41, pp.148-177, 1979.

D. Azriel, M. Mandel, and Y. Rinott, The treatment versus experimentation dilemma in dose finding studies, Journal of Statistical Planning and Inference, vol.141, issue.8, pp.2759-2768, 2011.
DOI : 10.1016/j.jspi.2011.03.001

P. Thall and H. Nguyen, Adaptive Randomization to Improve Utility-Based Dose-Finding with Bivariate Ordinal Outcomes, Journal of Biopharmaceutical Statistics, vol.25, issue.4, pp.785-801, 2012.
DOI : 10.1080/10543406.2012.676586

A. Oron and P. Hoff, Small-sample behavior of novel phase I cancer trial designs, Clinical Trials, vol.16, issue.2, pp.63-80, 2013.
DOI : 10.1002/sim.903

D. Berry and S. Eick, Adaptive assignment versus balanced randomization in clinical trials: A decision analysis, Statistics in Medicine, vol.58, issue.3, pp.231-246, 1995.
DOI : 10.1002/sim.4780140302

R. Sutton and A. Barto, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998.
DOI : 10.1109/TNN.1998.712192

S. Villar, J. Bowden, and J. Wason, Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges, Statistical Science, vol.30, issue.2, pp.199-215, 2015.
DOI : 10.1214/14-STS504

P. Thall and R. Simon, Practical Bayesian Guidelines for Phase IIB Clinical Trials, Biometrics, vol.50, issue.2, pp.337-349, 1994.
DOI : 10.2307/2533377

P. Thall, R. Millikan, P. Mueller, and S. Lee, Dose-Finding with Two Agents in Phase I Oncology Trials, Biometrics, vol.55, issue.3, pp.487-496, 2003.
DOI : 10.1111/1541-0420.00058

Y. Yuan and G. Yin, Bayesian dose finding by jointly modelling toxicity and efficacy as time-to-event outcomes, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.62, issue.5, pp.719-736, 2009.
DOI : 10.1111/j.1467-9876.2009.00674.x

G. Yin and Y. Yuan, Bayesian dose finding in oncology for drug combinations by copula regression, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.14, issue.2, pp.211-224, 2009.
DOI : 10.1111/j.1467-9876.2009.00649.x

G. Yin and Y. Yuan, A Latent Contingency Table Approach to Dose Finding for Combinations of Two Agents, Biometrics, vol.63, issue.3, pp.866-875, 2009.
DOI : 10.1111/j.1541-0420.2008.01119.x

B. Storer, Design and Analysis of Phase I Clinical Trials, Biometrics, vol.45, issue.3, pp.925-962, 1989.
DOI : 10.2307/2531693

E. Korn, D. Midthune, T. Chen, L. Rubinstein, M. Christian et al., A comparison of two phase I trial designs, Statistics in Medicine, vol.70, issue.18, pp.1799-806, 1994.
DOI : 10.1002/sim.4780131802

A. Ivanova, A. Montazer-haghighi, S. Mohanty, and S. Durham, Improved up-and-down designs for phase I trials, Statistics in Medicine, vol.86, issue.1, pp.69-82, 2003.
DOI : 10.1002/sim.1336

A. Ivanova and K. Wang, A non-parametric approach to the design and analysis of two-dimensional dose-finding trials, Statistics in Medicine, vol.23, issue.12, pp.1861-1870, 2004.
DOI : 10.1002/sim.1796

P. Thall and J. Cook, Dose-Finding Based on Efficacy-Toxicity Trade-Offs, Biometrics, vol.97, issue.3, pp.684-693, 2004.
DOI : 10.1111/j.0006-341X.2004.00218.x

F. Jiang, J. Lee, J. Muller, and P. , A Bayesian decision-theoretic sequential responseadaptive randomization design, pp.1975-1994, 2013.

J. Audibert, S. Bubeck, and R. Munos, Best Arm Identification in Multi-Armed Bandits, Proceedings of the 23th annual conference on Computational Learning Theory, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00654404

S. Kalyanakrishnan, A. Tewari, P. Auer, and P. Stone, PAC Subset Selection in Stochastic Multi-armed Bandits, proceedings of the 29th International Conference on Machine Learning (ICML), pp.655-662, 2012.