R. J. Lilford, J. G. Thornton, and D. Braunholtz, Clinical trials and rare diseases: a way out of a conundrum, BMJ, vol.311, pp.1621-1626, 1995.

S. Tan, K. Dear, P. Bruzzi, and D. Machin, Strategy for randomised clinical trials in rare cancers, BMJ, vol.327, pp.47-56, 2003.

, Guideline on clinical trials in small populations. Comittee for medicinal products for human use (CHMP), European Medical Agency, 2006.

L. Billingham, K. Malottki, and N. Steven, Small sample sizes in clinical trials: a statistician's perspective, Clin Investig, vol.2, pp.655-662, 2012.

E. L. Korn, L. M. Mcshane, and B. Freidlin, Statistical challenges in the evaluation of treatments for small patient populations, Sci Transl Med, vol.5, pp.178-181, 2013.

T. Smith, C. Williamson, P. R. Beresford, and M. W. , Methodology of clinical trials for rare diseases, Best Pract Res Clin Rheumatol, vol.28, pp.247-62, 2014.

M. Parmar, M. R. Sydes, and T. P. Morris, How do you design randomised trials for smaller populations? A framework, BMC Med, vol.14, p.183, 2016.

E. Council, on an action in the field of rare diseases, 2009.

N. Boyd, J. E. Dancey, C. B. Gilks, and D. G. Huntsman, Rare cancers: a sea of opportunity, Lancet Oncol, vol.17, pp.52-61, 2016.

J. Blay, J. Coindre, F. Ducimetière, and I. Ray-coquard, The value of research collaborations and consortia in rare cancers, Lancet Oncol, vol.17, pp.62-71, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01791267

L. Billingham, K. Malottki, and N. Steven, Research methods to change clinical practice for patients with rare cancers, Lancet Oncol, vol.17, pp.70-80, 2016.

H. Eichler, B. Bloechl-daum, P. Bauer, F. Bretz, J. Brown et al., Threshold-crossing": a useful way to establish the counterfactual in clinical trials?, Clin Pharmacol Ther, vol.100, pp.699-712, 2016.

D. Food and . Administration, Guidance for industry and FDA staff: Guidance for the Use of Bayesian Statistics in Medical Device Clinical Trials, 2016.

D. Food and . Administration, Antibacterial therapies for patients with an unmet medical need for the treatment of serious bacterial diseases. Guidance for Industry, 2017.

S. J. Pocock, The combination of randomized and historical controls in clinical trials, J Chronic Dis, vol.29, pp.175-88, 1976.

J. G. Ibrahim and M. Chen, Power prior distributions for regression models, Stat Sci, vol.15, pp.46-60, 2000.

B. Neuenschwander, M. Branson, and D. J. Spiegelhalter, A note on the power prior, Stat Med, vol.28, pp.3562-3568, 2009.

B. Neuenschwander, G. Capkun-niggli, M. Branson, and D. J. Spiegelhalter, Summarizing historical information on controls in clinical trials, Clin Trials Lond Engl, vol.7, pp.5-18, 2010.

B. P. Hobbs, B. P. Carlin, S. J. Mandrekar, and D. J. Sargent, Hierarchical commensurate and power prior models for adaptive incorporation of historical information in clinical trials, Biometrics, vol.67, pp.1047-56, 2011.

B. P. Hobbs, D. J. Sargent, and B. P. Carlin, Commensurate priors for incorporating historical information in clinical trials using general and generalized linear models, Bayesian Anal, vol.7, pp.639-74, 2012.

H. Schmidli, S. Gsteiger, S. Roychoudhury, A. O'hagan, D. Spiegelhalter et al., Robust meta-analytic-predictive priors in clinical trials with historical control information, Biometrics, vol.70, pp.1023-1055, 2014.

T. Mutsvari, D. Tytgat, and R. Walley, Addressing potential prior-data conflict when using informative priors in proof-of-concept studies, Pharm Stat, vol.15, pp.28-36, 2016.

J. X. Li, W. Chen, and J. A. Scott, Addressing prior-data conflict with empirical meta-analytic-predictive priors in clinical studies with historical information, J Biopharm Stat, vol.26, pp.1056-66, 2016.

I. Gravestock and L. Held, Adaptive power priors with empirical Bayes for clinical trials, Pharm Stat, vol.16, pp.349-60, 2017.

I. Wadsworth, L. V. Hampson, and T. Jaki, Extrapolation of efficacy and other data to support the development of new medicines for children: a systematic review of methods, Stat Methods Med Res, p.0962280216631359, 2016.

C. Hsiao, Y. Hsu, H. Tsou, and J. Liu, Use of prior information for Bayesian evaluation of bridging studies, J Biopharm Stat, vol.17, pp.109-130, 2007.

M. Gandhi, B. Mukherjee, and D. Biswas, A Bayesian approach for inference from a bridging study with binary outcomes, J Biopharm Stat, vol.22, pp.935-51, 2012.

J. Berger and L. M. Berliner, Robust Bayes and empirical Bayes analysis with $_\epsilon$-contaminated priors, Ann Stat, vol.14, pp.461-86, 1986.

J. B. Greenhouse and H. Seltman, Using prior distributions to synthesize historical evidence: comments on the Goodman-Sladky case study of IVIg in Guillain-Barré syndrome, Clin Trials Lond Engl, vol.2, pp.364-78, 2005.

C. Brard, L. Teuff, G. , L. Deley, M. Hampson et al., Bayesian survival analysis in clinical trials: what methods are used in practice?, Clin Trials, vol.14, pp.78-87, 2017.

J. Bogaerts, M. R. Sydes, N. Keat, A. Mcconnell, A. Benson et al., Clinical trial designs for rare diseases: studies developed and discussed by the international rare cancers initiative, Eur J Cancer Oxf Engl, vol.51, pp.271-281, 1990.

S. Piperno-neumann, M. Deley, F. Rédini, H. Pacquement, P. Marec-bérard et al., Zoledronate in combination with chemotherapy and surgery to treat osteosarcoma (OS2006): a randomised, multicentre, open-label, Lancet Oncol, vol.17, pp.1070-80, 2016.
URL : https://hal.archives-ouvertes.fr/inserm-01702092

P. A. Meyers, C. L. Schwartz, M. D. Krailo, J. H. Healey, M. L. Bernstein et al., Osteosarcoma: the addition of muramyl tripeptide to chemotherapy improves overall survival--a report from the Children's oncology group, J Clin Oncol, vol.26, pp.633-641, 2008.

A. J. Chou, E. S. Kleinerman, M. D. Krailo, Z. Chen, D. L. Betcher et al., Addition of muramyl tripeptide to chemotherapy for patients with newly diagnosed metastatic osteosarcoma: a report from the Children's oncology group, Cancer, vol.115, pp.5339-5387, 2009.

D. A. Schoenfeld, Sample-size formula for the proportional-hazards regression model, Biometrics, vol.39, pp.499-503, 1983.

A. A. Tsiatis, The asymptotic joint distribution of the efficient scores test for the proportional hazards model calculated over time, Biometrika, vol.68, pp.311-316, 1981.

W. K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, vol.57, pp.97-109, 1970.

. R-core-team, R Foundation for statistical computing. R: A Language and Environment for Statistical Computing, 2016.

A. Martin, K. Quinn, and P. J. Mcmcpack, Markov chain Monte Carlo in R, J Stat Softw, vol.42, 2011.

J. Albert, LearnBayes: functions for learning Bayesian inference, 2014.

R. L. Cuffe, The inclusion of historical control data may reduce the power of a confirmatory study, Stat Med, vol.30, pp.1329-1367, 2011.

K. Viele, S. Berry, B. Neuenschwander, B. Amzal, F. Chen et al., Use of historical control data for assessing treatment effects in clinical trials, Pharm Stat, vol.13, pp.41-54, 2014.

J. Van-rosmalen, D. Dejardin, Y. Van-norden, B. Löwenberg, and E. Lesaffre, Including historical data in the analysis of clinical trials: is it worth the effort?, Stat Methods Med Res, p.962280217694506, 2017.

S. Gsteiger, B. Neuenschwander, F. Mercier, and H. Schmidli, Using historical control information for the design and analysis of clinical trials with overdispersed count data, Stat Med, vol.32, pp.3609-3631, 2013.

B. Neelon, O. Malley, and A. , The use of power prior distributions for incorporating historical data into a Bayesian analysis. Technical report: Dep Health Care Policy Harv Med Sch, 2009.

C. Rietbergen, I. Klugkist, K. Janssen, K. Moons, and H. Hoijtink, Incorporation of historical data in the analysis of randomized therapeutic trials, Contemp Clin Trials, vol.32, pp.848-55, 2011.

P. Royston and M. Parmar, Flexible parametric proportional-hazards and proportionalodds models for censored survival data, with application to prognostic modelling and estimation of treatment effects, Stat Med, vol.21, pp.2175-97, 2002.

J. G. Ibrahim, M. Chen, H. A. Xia, and T. Liu, Bayesian meta-experimental design: evaluating cardiovascular risk in new antidiabetic therapies to treat type 2 diabetes, Biometrics, vol.68, pp.578-86, 2012.

E. Tavernier, L. Trinquart, and B. Giraudeau, Finding alternatives to the dogma of power based sample size calculation: is a fixed sample size prospective meta-experiment a potential alternative, PLoS One, vol.11, p.158604, 2016.