E. Azoulay, C. Adrie, D. Lassence, A. Pochard, F. Moreau et al., Determinants of postintensive care unit mortality: A prospective multicenter study, Critical Care Medicine, vol.31, issue.2, pp.428-432, 2003.
DOI : 10.1097/01.CCM.0000048622.01013.88

P. Vanhems, A. Lepape, A. Savey, P. Jambou, and J. Fabry, Nosocomial pulmonary infection by antimicrobial-resistant bacteria of patients hospitalized in intensive care units: risk factors and survival, Journal of Hospital Infection, vol.45, issue.2, pp.98-106, 2000.
DOI : 10.1053/jhin.2000.0752

E. Azoulay, C. Alberti, C. Bornstain, G. Leleu, D. Moreau et al., Improved survival in cancer patients requiring mechanical ventilatory support: Impact of noninvasive mechanical ventilatory support, Critical Care Medicine, vol.29, issue.3, pp.519-525, 2001.
DOI : 10.1097/00003246-200103000-00009

J. De-irala-estevez, D. Martinez-concha, C. Diaz-molina, J. Masa-calles, S. Del-castillo et al., Comparison of different methodological approaches to identify risk factors of nosocomial infection in intensive care units, Intensive Care Medicine, vol.27, issue.8, pp.1254-1262, 2001.
DOI : 10.1007/s001340101007

P. Andersen, S. Abildstrom, and S. Rosthoj, Competing risks as a multi-state model, Statistical Methods in Medical Research, vol.11, issue.2, pp.203-215, 2002.
DOI : 10.1191/0962280202sm281ra

M. Pepe and M. Mori, Kaplan???meier, marginal or conditional probability curves in summarizing competing risks failure time data?, Statistics in Medicine, vol.34, issue.8, pp.737-751, 1993.
DOI : 10.1002/sim.4780120803

J. Fine and R. Gray, A Proportional Hazards Model for the Subdistribution of a Competing Risk, Journal of the American Statistical Association, vol.69, issue.446, pp.496-509, 1999.
DOI : 10.2307/2530374

G. Rubenfeld, Looking beyond 28-day all-cause mortality, Critical Care, vol.6, issue.4, pp.293-294, 2002.
DOI : 10.1186/cc1513

E. Azoulay, G. Thiéry, S. Chevret, D. Moreau, M. Darmon et al., The Prognosis of Acute Respiratory Failure in Critically Ill Cancer Patients, Medicine, vol.83, issue.6, pp.360-70, 2004.
DOI : 10.1097/01.md.0000145370.63676.fb

L. Gall, J. Klar, J. Lemeshow, S. Saulnier, F. Alberti et al., The Logistic Organ Dysfunction system. A new way to assess organ dysfunction in the intensive care unit. ICU Scoring Group, JAMA: The Journal of the American Medical Association, vol.276, issue.10, pp.802-810, 1996.
DOI : 10.1001/jama.276.10.802

R. Foundation and . Computing, R: a language and environment for statistical computing (version 2.0.1)

C. Alberti, F. Metivier, P. Landais, E. Thervet, C. Legendre et al., Improving estimates of event incidence over time in populations exposed to other events, Journal of Clinical Epidemiology, vol.56, issue.6, pp.536-545, 2003.
DOI : 10.1016/S0895-4356(03)00058-1

C. Alberti, C. Brun-buisson, S. Chevret, M. Antonelli, S. Goodman et al., Systemic Inflammatory Response and Progression to Severe Sepsis in Critically Ill Infected Patients, American Journal of Respiratory and Critical Care Medicine, vol.171, issue.5, pp.461-468, 2005.
DOI : 10.1164/rccm.200403-324OC

K. Leffondre, M. Abrahamowicz, and J. Siemiatycki, Evaluation of Cox's model and logistic regression for matched case-control data with time-dependent covariates: a simulation study, Statistics in Medicine, vol.82, issue.24, pp.3781-3794, 2003.
DOI : 10.1002/sim.1674

S. Castaigne, S. Chevret, E. Archimbaud, P. Fenaux, D. Bordessoule et al., Randomized comparison of double induction and timed-sequential induction to a "3 + 7" induction in adults with AML: long-term analysis of the Acute Leukemia French Association (ALFA) 9000 study, Blood, vol.104, issue.8, pp.2467-74, 2004.
DOI : 10.1182/blood-2003-10-3561

M. Colleoni, O. Neill, A. Goldhirsch, A. Gelber, R. Bonetti et al., Identifying Breast Cancer Patients at High Risk for Bone Metastases, Journal of Clinical Oncology, vol.18, issue.23, pp.3925-3935, 2000.
DOI : 10.1200/JCO.2000.18.23.3925

S. De-botton, V. Coiteux, S. Chevret, C. Rayon, E. Vilmer et al., -Retinoic Acid and Chemotherapy, Journal of Clinical Oncology, vol.22, issue.8, pp.1404-1412, 2004.
DOI : 10.1200/JCO.2004.09.008
URL : https://hal.archives-ouvertes.fr/jpa-00214712

G. Martelli, R. Miceli, D. Palo, G. Coradini, D. Salvadori et al., Is axillary lymph node dissection necessary in elderly patients with breast carcinoma who have a clinically uninvolved axilla? Cancer, pp.1156-1163, 2003.

M. Robson, P. Chappuis, J. Satagopan, N. Wong, J. Boyd et al., A combined analysis of outcome following breast cancer: differences in survival based on BRCA1/BRCA2 mutation status and administration of adjuvant treatment, Breast Cancer Research, vol.20, issue.2, pp.8-17, 2004.
DOI : 10.1200/JCO.20.6.1480

E. Shpall, R. Quinones, R. Giller, C. Zeng, A. Baron et al., Transplantation of ex vivo expanded cord blood, Biology of Blood and Marrow Transplantation, vol.8, issue.7, pp.368-376, 2002.
DOI : 10.1053/bbmt.2002.v8.pm12171483

D. Altman, J. Deeks, and D. Sackett, Odds ratios should be avoided when events are common, BMJ, vol.317, issue.7168, p.1318, 1998.
DOI : 10.1136/bmj.317.7168.1318
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1114216

M. Symons and D. Moore, Hazard rate ratio and prospective epidemiological studies, Journal of Clinical Epidemiology, vol.55, issue.9, pp.893-899, 2002.
DOI : 10.1016/S0895-4356(02)00443-2

R. Cook and S. Weisberg, Residuals and In uence in Regression, 1982.

R. Cook, Assessment of local in uence, Journal of the Royal Statistical Society B, vol.48, pp.133-169, 1986.

L. Escobar and W. Meeker, Assessing Influence in Regression Analysis with Censored Data, Biometrics, vol.48, issue.2, pp.507-528, 1992.
DOI : 10.2307/2532306

S. Chatterjee and A. Hadi, Influential Observations, High Leverage Points, and Outliers in Linear Regression, Statistical Science, vol.1, issue.3, pp.379-416, 1986.
DOI : 10.1214/ss/1177013622

K. Cain and N. Lange, Approximate Case Influence for the Proportional Hazards Regression Model with Censored Data, Biometrics, vol.40, issue.2, pp.493-499, 1984.
DOI : 10.2307/2531402

N. Reid and H. Cr-epeau, Influence functions for proportional hazards regression, Biometrika, vol.72, issue.1, pp.1-9, 1985.
DOI : 10.1093/biomet/72.1.1

C. Chen and P. Wang, Diagnostic Plots in Cox's Regression Model, Biometrics, vol.47, issue.3, pp.841-850, 1991.
DOI : 10.2307/2532643

H. Zhu and H. Zhang, A diagnostic procedure based on local influence, Biometrika, vol.91, issue.3, pp.579-589, 2004.
DOI : 10.1093/biomet/91.3.579

A. Pettitt, B. Daud, and I. , Case-Weighted Measures of Influence for Proportional Hazards Regression, Applied Statistics, vol.38, issue.1, pp.51-67, 1989.
DOI : 10.2307/2347680

D. Machin, On the evolution of statistical methods as applied to clinical trials, Journal of Internal Medicine, vol.175, issue.5, pp.521-528, 2004.
DOI : 10.1046/j.1365-2141.1996.d01-1802.x

P. Andersen, S. Abildstrm, and S. Rosthj, Competing risks as a multi-state model, Statistical Methods in Medical Research, vol.11, issue.2, pp.203-215, 2002.
DOI : 10.1191/0962280202sm281ra

R. Prentice, J. Kalb-eisch, A. Peterson, N. Flournoy, V. Farewell et al., The Analysis of Failure Times in the Presence of Competing Risks, Biometrics, vol.34, issue.4, pp.541-554, 1978.
DOI : 10.2307/2530374

R. Gray, A Class of $K$-Sample Tests for Comparing the Cumulative Incidence of a Competing Risk, The Annals of Statistics, vol.16, issue.3, pp.1141-1154, 1988.
DOI : 10.1214/aos/1176350951

S. Pepe, Inference for Events with Dependent Risks in Multiple Endpoint Studies, Journal of the American Statistical Association, vol.304, issue.415, pp.770-778, 1991.
DOI : 10.1080/01621459.1989.10478873

J. Fine, Regression modeling of competing crude failure probabilities, Biostatistics, vol.2, issue.1, pp.85-97, 2001.
DOI : 10.1093/biostatistics/2.1.85

J. Klein and P. Andersen, Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function, Biometrics, vol.15, issue.1, pp.223-229, 2005.
DOI : 10.1093/biomet/85.4.980

D. Schoenfeld, Partial residuals for the proportional hazards regression model, Biometrika, vol.69, issue.1, pp.239-241, 1982.
DOI : 10.1093/biomet/69.1.239

P. Fenaux, C. Chastang, S. Chevret, M. Sanz, H. Dombret et al., A randomized comparison of all transretinoic acid (ATRA) followed by chemotherapy and ATRA plus chemotherapy and the role of maintenance therapy in newly diagnosed acute promyelocytic leukemia, Blood, vol.94, pp.1192-1200, 1999.

D. Dupuis and D. Hamilton, Regression residuals and test statistics: Assessing naive outlier deletion, Canadian Journal of Statistics, vol.64, issue.2, pp.259-275, 2000.
DOI : 10.2307/3315977

A. Welsh and E. Ronchetti, A journey in single steps: robust one-step M-estimation in linear regression, Journal of Statistical Planning and Inference, vol.103, issue.1-2, pp.287-310, 2002.
DOI : 10.1016/S0378-3758(01)00228-2

Y. Hochberg and Y. Benjamini, More powerful procedures for multiple significance testing, Statistics in Medicine, vol.63, issue.7, pp.811-818, 1990.
DOI : 10.1002/sim.4780090710

S. Chevret, Basic concepts in dose-finding. In Statistical Methods for dose-finding experiments, Chevret SE, pp.5-18, 2006.

E. Korn, S. Arbuck, J. Pluda, R. Simon, R. Kaplan et al., Clinical Trial Designs for Cytostatic Agents: Are New Approaches Needed?, Journal of Clinical Oncology, vol.19, issue.1, pp.265-272, 2001.
DOI : 10.1200/JCO.2001.19.1.265

B. Storer, Design and Analysis of Phase I Clinical Trials, Biometrics, vol.45, issue.3, pp.925-937, 1989.
DOI : 10.2307/2531693

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

C. Gatsonis and J. Greenhouse, Bayesian methods for phase I clinical trials, Statistics in Medicine, vol.81, issue.10, pp.1377-1389, 1992.
DOI : 10.1002/sim.4780111011

J. Whitehead and H. Brunier, BAYESIAN DECISION PROCEDURES FOR DOSE DETERMINING EXPERIMENTS, Statistics in Medicine, vol.32, issue.9, pp.885-893, 1995.
DOI : 10.1002/sim.4780140904

J. Babb, A. Rogatko, and S. Zacks, Cancer phase I clinical trials: efficient dose escalation with overdose control, Statistics in Medicine, vol.17, issue.10, pp.1103-1120, 1998.
DOI : 10.1002/(SICI)1097-0258(19980530)17:10<1103::AID-SIM793>3.0.CO;2-9

P. Thall and K. Russell, A Strategy for Dose-Finding and Safety Monitoring Based on Efficacy and Adverse Outcomes in Phase I/II Clinical Trials, Biometrics, vol.54, issue.1, pp.251-264, 1998.
DOI : 10.2307/2534012

M. Gasparini and J. Eisele, A Curve-Free Method for Phase I Clinical Trials, Biometrics, vol.11, issue.2, pp.609-615, 2000.
DOI : 10.1111/j.0006-341X.2000.00609.x

D. Faries, Practical modifications of the continual reassessment method for phase i cancer clinical trials, Journal of Biopharmaceutical Statistics, vol.31, issue.2, pp.147-164, 1994.
DOI : 10.1111/j.1472-8206.1990.tb00079.x

S. Goodman, M. Zahurak, and S. Piantadosi, Some practical improvements in the continual reassessment method for phase I studies, Statistics in Medicine, vol.12, issue.11, pp.1149-1161, 1995.
DOI : 10.1002/sim.4780141102

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.1799-1806, 1994.
DOI : 10.1002/sim.4780131802

S. Moller, AN EXTENSION OF THE CONTINUAL REASSESSMENT METHODS USING A PRELIMINARY UP-AND-DOWN DESIGN IN A DOSE FINDING STUDY IN CANCER PATIENTS, IN ORDER TO INVESTIGATE A GREATER RANGE OF DOSES, Statistics in Medicine, vol.48, issue.9, pp.911-922, 1995.
DOI : 10.1002/sim.4780140909

O. Quigley, J. Shen, and L. , Continual Reassessment Method: A Likelihood Approach, Biometrics, vol.52, issue.2, pp.673-684, 1996.
DOI : 10.2307/2532905

N. Ting, Dose Finding in Drug development, 2006.
DOI : 10.1007/0-387-33706-7

R. Roberts, W. Rodriguez, D. Murphy, and T. Crescenzi, Pediatric drug labeling. Improving the safety and efficacy of pediatrics therapies, JAMA, 2003.

B. Storer, Statistics in Clinical Oncology, pp.73-91, 2001.

F. Lefrere, S. Zohar, J. Bresson, S. Chevret, A. Mogenet et al., A double-blind low dose-finding phase II study of granulocyte colony-stimulating factor combined with chemotherapy for stem cell mobilization in patients with non-hodgkin's lymphoma, Haematologica, vol.91, pp.550-553, 2006.

J. Carvalho, M. Balki, J. Kingdom, and R. Windrim, Oxytocin Requirements at Elective Cesarean Delivery: A Dose-Finding Study, Obstetrics & Gynecology, vol.104, issue.5, Part 1, pp.1005-1010, 2004.
DOI : 10.1097/01.AOG.0000142709.04450.bd

J. Copas, Regression, prediction and shrinkage (with discussion), Journal of the Royal Statistical Society, Series B, vol.45, pp.311-354, 1983.

G. Wetherill and K. Glazebrook, Sequential Methods in Statistics, 1986.

J. Wierda, O. Meretoja, T. Taivainen, and J. Proost, Pharmacokinetics and pharmacokinetic-dynamic modelling of rocuronium in infants and children, British Journal of Anaesthesia, vol.78, issue.6, pp.690-695, 1997.
DOI : 10.1093/bja/78.6.690

G. Scheiber, F. Ribeiro, A. Marichal, M. Bredendiek, and K. Renzing, Intubating conditions and onset of action after rocuronium in young children, Pediatric Anesthesia, vol.83, pp.320-324, 1996.

J. Whitehead and D. Williamson, Bayesian decision procedures based on logistic regression models for dose-finding studies, Journal of Biopharmaceutical Statistics, vol.8, issue.3, pp.445-467, 1998.
DOI : 10.1080/10543409808835252

D. Cook, Assessment of local influence, Journal of the Royal Statistical Society, Series B, vol.48, pp.133-169, 1986.

J. Pan, K. Fang, and D. Von-rosen, Local influence assessment in the growth curve model with unstructured covariance, Journal of Statistical Planning and Inference, vol.62, issue.2, pp.263-278, 1997.
DOI : 10.1016/S0378-3758(96)00194-2

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.69-821, 2003.
DOI : 10.1002/sim.1336

J. O. Quigley, M. Pepe, and L. Fisher, 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

C. Gatsonis and J. B. Greenhouse, Bayesian methods for phase I clinical trials, Statistics in Medicine, vol.81, issue.10, pp.1377-1389, 1992.
DOI : 10.1002/sim.4780111011

J. Whitehead and H. Brunier, BAYESIAN DECISION PROCEDURES FOR DOSE DETERMINING EXPERIMENTS, Statistics in Medicine, vol.32, issue.9, pp.885-893, 1995.
DOI : 10.1002/sim.4780140904

P. F. Thall and K. E. Russell, A Strategy for Dose-Finding and Safety Monitoring Based on Efficacy and Adverse Outcomes in Phase I/II Clinical Trials, Biometrics, vol.54, issue.1, pp.251-264, 1998.
DOI : 10.2307/2534012

J. O. Quigley and L. Z. Shen, Continual Reassessment Method: A Likelihood Approach, Biometrics, vol.52, issue.2, pp.673-684, 1996.
DOI : 10.2307/2532905

J. B. Copas, Binary regression models for contaminated data, Journal of the royal statistical society. Series B, vol.50, pp.225-265, 1988.

V. and R. Joseph, Efficient Robbins-Monro procedure for binary data, Biometrika, vol.91, issue.2, pp.461-470, 2004.
DOI : 10.1093/biomet/91.2.461

D. M. Potter, Phase I Studies of Chemotherapeutic Agents in Cancer Patients: A Review of the Designs, Journal of Biopharmaceutical Statistics, vol.8, issue.5, pp.579-604, 2006.
DOI : 10.1081/BIP-120017728

F. Hu and W. F. Rosenberger, Analysis of time trends in adaptive designs with application to a neurophysiology experiment, Statistics in Medicine, vol.9, issue.15, pp.2067-2042, 2000.
DOI : 10.1002/1097-0258(20000815)19:15<2067::AID-SIM508>3.0.CO;2-L

F. Hu and W. F. Rosenberger, The Theory of Response-Adaptive Randomization in Clinical Trials Wiley series in probability and statistics, pp.107-119, 2006.

F. Hu and J. V. Zidek, The weighted likelihood. The Canadian journal of statistics, pp.347-371, 2002.

F. Hu, The asymptotic properties of the maximum relevance weighted likelihood estimators. The Canadian journal of statistics, pp.45-49, 1997.

M. P. Victoria-feser, Robust inference with binary data, Psychometrika, vol.51, issue.427, pp.21-32, 2002.
DOI : 10.1007/BF02294707
URL : http://archive-ouverte.unige.ch/unige:6456

A. F. Ruckstuhl and A. H. Welsh, Robust fitting of the binomial model. The annals of statistics, pp.1117-1136, 2001.

J. O. Quigley, Retrospective analysis of sequential dose-finding designs, Biometrics Ltd. Statist. Med, vol.61, pp.749-7561, 2005.

Y. K. Cheung and R. Chappell, Sequential Designs for Phase I Clinical Trials with Late-Onset Toxicities, Biometrics, vol.14, issue.Suppl., pp.1177-82, 2000.
DOI : 10.1111/j.0006-341X.2000.01177.x

J. O. Quigley and E. Reiner, Miscellanea. A stopping rule for the continual reassessment method, Biometrika, vol.85, issue.3, pp.741-748, 1998.
DOI : 10.1093/biomet/85.3.741

S. Zohar and S. Chevret, The continual reassessment method: comparison of Bayesian stopping rules for dose-ranging studies, Statistics in Medicine, vol.54, issue.19, pp.2827-2843, 2001.
DOI : 10.1002/sim.920

]. G. Bibliographie1, N. R. Box, and . Draper, Empirical Model-Building and Response Surfaces, Wiley Series in Probability and Mathematical Statistics. Applied probability and Statistics, 1987.

R. D. Cook and S. Weisberg, Residuals and Influence in Regression, 1982.

D. Cook, Assessment of local influence, Journal of the Royal Statistical Society, Series B, vol.48, pp.133-169, 1986.

G. A. Barnard, Discussion of professor Box's paper, Journal of the Royal Statistical Society A, vol.143, pp.404-406, 1980.

L. A. Escobar and W. Meeker, Assessing Influence in Regression Analysis with Censored Data, Biometrics, vol.48, issue.2, pp.507-528, 1992.
DOI : 10.2307/2532306

C. H. Chen and P. C. Wang, Diagnostic Plots in Cox's Regression Model, Biometrics, vol.47, issue.3, pp.841-850, 1991.
DOI : 10.2307/2532643

D. Cook, Detection of influential observations in linear regression, Technometrics, vol.19, pp.1-12, 1977.

D. Pregibon, Logistic Regression Diagnostics, The Annals of Statistics, vol.9, issue.4, pp.705-724, 1981.
DOI : 10.1214/aos/1176345513

K. C. Cain and N. T. Lange, Approximate Case Influence for the Proportional Hazards Regression Model with Censored Data, Biometrics, vol.40, issue.2, pp.493-499, 1984.
DOI : 10.2307/2531402

D. Collett, Modelling survival data in medical research, 2003.
DOI : 10.1007/978-1-4899-3115-3

D. R. Cox, Regression models and life tables (with discussion), Journal of the Royal Statistical Society B, vol.34, pp.187-220, 1977.

J. P. Fine and R. J. Gray, A Proportional Hazards Model for the Subdistribution of a Competing Risk, Journal of the American Statistical Association, vol.69, issue.446, pp.496-509, 1999.
DOI : 10.2307/2530374

J. O. Quigley, M. Pepe, and L. Fisher, 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

P. Mccullagh and J. A. Nelder, Generalized Linear Models, 1989.

F. E. Harrell, Regression modeling strategies, 2001.

D. A. Belsley, E. Kuh, and R. E. Welsch, Regression diagnostics, 1980.
DOI : 10.1002/0471725153

D. Pregibon, Resistant Fits for Some Commonly Used Logistic Models with Medical Applications, Biometrics, vol.38, issue.2, pp.485-498, 1982.
DOI : 10.2307/2530463

D. A. Williams, Generalized Linear Model Diagnostics Using the Deviance and Single Case Deletions, Applied Statistics, vol.36, issue.2, pp.181-191, 1987.
DOI : 10.2307/2347550

A. J. Lawrance, Deletion and masking in regression, Journal of the Royal Statistical Society B, vol.57, pp.181-189, 1995.

W. Thomas and R. D. Cook, Assessing influence on regression coefficients in generalized linear models, Biometrika, vol.76, issue.4, pp.741-749, 1989.
DOI : 10.1093/biomet/76.4.741

H. T. Zhu and L. S. , Local influence for generalized linear models. The Canandian journal of statistics, pp.293-309, 2003.

W. Y. Poon and Y. S. Poon, Conformal normal curvature and assessment of local influence, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.61, issue.1, pp.51-61, 1999.
DOI : 10.1111/1467-9868.00162

A. N. Pettitt and I. B. Daud, Case-Weighted Measures of Influence for Proportional Hazards Regression, Applied Statistics, vol.38, issue.1, pp.51-67, 1989.
DOI : 10.2307/2347680

X. Wu and Z. Luo, Second-order approach to local influence, Journal of the Royal Statistical Society B, vol.55, pp.929-936, 1993.

H. Zhu and H. Zhang, A diagnostic procedure based on local influence, Biometrika, vol.91, issue.3, pp.579-589, 2004.
DOI : 10.1093/biomet/91.3.579

E. Lesaffre and G. Verbeke, Local Influence in Linear Mixed Models, Biometrics, vol.54, issue.2, pp.570-582, 1998.
DOI : 10.2307/3109764

E. Demidenko and T. A. Stukel, Influence analysis for linear mixed-effects models, Statistics in Medicine, vol.42, issue.6, pp.893-909, 2005.
DOI : 10.1002/sim.1974

K. Van-steen, G. Molenberghs, G. Verbeke, and H. Thijs, A local influence approach to sensitivity analysis of incomplete longitudinal ordinal data, Statistical Modelling, vol.1, issue.2, pp.125-142, 2001.
DOI : 10.1191/147108201128122

M. Galea-rojas, H. Bolfarine, and M. D. Castro, Local Influence in Comparative Calibration Models, Biometrical Journal, vol.50, issue.1, pp.59-81, 2002.
DOI : 10.1002/1521-4036(200201)44:1<59::AID-BIMJ59>3.0.CO;2-2

R. J. Gray, A Class of $K$-Sample Tests for Comparing the Cumulative Incidence of a Competing Risk, The Annals of Statistics, vol.16, issue.3, pp.1141-1154, 1988.
DOI : 10.1214/aos/1176350951

M. S. Pepe, Inference for Events with Dependent Risks in Multiple Endpoint Studies, Journal of the American Statistical Association, vol.304, issue.415, pp.770-778, 1991.
DOI : 10.1080/01621459.1989.10478873

T. A. Gooley, W. Leisenring, J. Crowley, and B. E. Storer, Estimation of failure probabilities in the presence of competing risks: new representations of old estimators, Statistics in Medicine, vol.1, issue.6, pp.695-706, 1999.
DOI : 10.1002/(SICI)1097-0258(19990330)18:6<695::AID-SIM60>3.0.CO;2-O

M. Crowder, Classical competing risks, 2001.
DOI : 10.1201/9781420035902

E. Azoulay, C. Adrie, A. De-lassence, F. Pochard, D. Moreau et al., Determinants of postintensive care unit mortality: A prospective multicenter study, Critical Care Medicine, vol.31, issue.2, pp.428-460, 2003.
DOI : 10.1097/01.CCM.0000048622.01013.88

J. R. Le, J. Gall, S. Klar, F. Lemeshow, C. Saulnier et al., The logistic organ dysfunction system. A new way to assess organ dysfunction in the intensive care unit. ICU scoring group, JAMA, vol.276, pp.802-812, 1996.

J. R. Le, S. Gall, G. Lemeshow, J. Leleu, J. Klar et al., Customized probability models for early severe sepsis in adult intensive care patients. Intensive care unit scoring group, JAMA, vol.273, pp.644-50, 1995.

J. R. Le, S. Gall, F. Lemeshow, and . Saulnier, A new simplified acute physiology score (SAPS II) based on a european/north american multicenter stu, JAMA, vol.270, pp.2957-63, 1993.

S. Lemeshow, J. R. Le, and . Gall, Modeling the severity of illness of ICU patients. A systems update, JAMA: The Journal of the American Medical Association, vol.272, issue.13, pp.1049-55, 1994.
DOI : 10.1001/jama.272.13.1049

S. Lemeshow, D. Teres, J. Klar, J. S. Avrunin, S. H. Gehlbach et al., Mortality Probability Models (MPM II) Based on an International Cohort of Intensive Care Unit Patients, JAMA: The Journal of the American Medical Association, vol.270, issue.20
DOI : 10.1001/jama.1993.03510200084037

D. J. Sirio, T. Murphy, A. Lotring, and . Damiano, The APACHE III prognostic system. Risk prediction of hospital mortality forcritically ill hospitalized adults, Chest, vol.100, pp.1619-1655, 1991.

D. Schoenfeld, Partial residuals for the proportional hazards regression model, Biometrika, vol.69, issue.1, pp.239-241, 1982.
DOI : 10.1093/biomet/69.1.239

P. M. Grambsch and T. M. Therneau, Proportional hazards tests and diagnostics based on weighted residuals, Biometrika, vol.81, issue.3, pp.515-526, 1994.
DOI : 10.1093/biomet/81.3.515

N. Reid and H. Crépeau, Influence functions for proportional hazards regression, Biometrika, vol.72, issue.1, pp.1-9, 1985.
DOI : 10.1093/biomet/72.1.1

B. E. Storer and J. Crowley, A Diagnostic for Cox Regression and General Conditional Likelihoods, Journal of the American Statistical Association, vol.37, issue.389, pp.139-147, 1985.
DOI : 10.1080/01621459.1985.10477153

W. E. Barlow, Global Measures of Local Influence for Proportional Hazards Regression Models, Biometrics, vol.53, issue.3, pp.1157-1162, 1997.
DOI : 10.2307/2533574

C. B. Parker and E. R. Delong, A Diagnostic for Cox Regression with Discrete Failure-Time Models, Biometrics, vol.80, issue.4, pp.996-1001, 2000.
DOI : 10.1111/j.0006-341X.2000.0996.x

N. Sartori, K. Thomaseth, and A. Salvan, Local influence analysis when interfacing toxicokinetic and proportional hazard models, Statistics in Medicine, vol.23, issue.15, pp.2399-2412, 2004.
DOI : 10.1002/sim.1823

W. H. Wei and J. S. Su, Model Choice and Influential Cases for Survival Studies, Biometrics, vol.75, issue.4, pp.1295-1299, 1999.
DOI : 10.1111/j.0006-341X.1999.01295.x

W. H. Wei and K. M. , Masking Unmasked in the Proportional Hazards Model, Biometrics, vol.30, issue.4, pp.991-995, 2000.
DOI : 10.1111/j.0006-341X.2000.0991.x

A. Latouche, R. Porcher, and S. Chevret, Sample size formula for proportional hazards modeling of competing risks, Proceedings of the First Barcelona Workshop on Survival Analysis, pp.91-92, 2002.

A. Nardi and M. Schemper, New Residuals for Cox Regression and Their Application to Outlier Screening, Biometrics, vol.77, issue.2, pp.523-529, 1999.
DOI : 10.1111/j.0006-341X.1999.00523.x

S. Chevret, Statistical methods dor dose-finding experiments, 2006.

C. Gatsonis and J. B. Greenhouse, Bayesian methods for phase I clinical trials, Statistics in Medicine, vol.81, issue.10, pp.1377-1389, 1992.
DOI : 10.1002/sim.4780111011

Y. Lin and W. J. Shih, Statistical properties of the traditional algorithm-based designs for phase I cancer clinical trials, Biostatistics, vol.2, issue.2, pp.203-218, 2001.
DOI : 10.1093/biostatistics/2.2.203

W. J. Shih and Y. Lin, Traditional and modified algorithm-based designs for phase I cancer clinical trials, editor, Statistical Methods for dose-finding experiments, pp.61-90, 2006.

M. Gezmu and N. Flournoy, Group up-and-down designs for dose-finding, Journal of Statistical Planning and Inference, vol.136, issue.6, pp.1749-1764, 2006.
DOI : 10.1016/j.jspi.2005.08.002
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.210.9262

J. O. Quigley and S. Chevret, Methods for dose finding studies in cancer clinical trials: A review and results of a monte carlo study, Statistics in Medicine, vol.29, issue.11, pp.1647-1664, 1991.
DOI : 10.1002/sim.4780101104

J. Whitehead and H. Brunier, BAYESIAN DECISION PROCEDURES FOR DOSE DETERMINING EXPERIMENTS, Statistics in Medicine, vol.32, issue.9, pp.885-893, 1995.
DOI : 10.1002/sim.4780140904

J. O. Quigley and L. Z. Shen, Continual Reassessment Method: A Likelihood Approach, Biometrics, vol.52, issue.2, pp.673-684, 1996.
DOI : 10.2307/2532905

E. L. Korn, D. Midthune, T. T. Chen, L. V. Rubinstein, M. C. Christian et al., A comparison of two phase I trial designs, Statistics in Medicine, vol.70, issue.18, pp.1799-1806, 1994.
DOI : 10.1002/sim.4780131802

S. N. Goodman, M. L. Zahurak, and S. Piantadosi, Some practical improvements in the continual reassessment method for phase I studies, Statistics in Medicine, vol.12, issue.11, pp.1149-1161, 1995.
DOI : 10.1002/sim.4780141102

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

C. Ahn, An evaluation of phase I cancer clinical trial designs, Statistics in Medicine, vol.17, issue.14, pp.1537-1586, 1998.
DOI : 10.1002/(SICI)1097-0258(19980730)17:14<1537::AID-SIM872>3.0.CO;2-F

J. M. Heyd and B. P. Carlin, Adaptive design improvements in the continual reassessment method for phase I studies, Statistics in Medicine, vol.85, issue.11, pp.1307-1321, 1999.
DOI : 10.1002/(SICI)1097-0258(19990615)18:11<1307::AID-SIM128>3.0.CO;2-X

S. Piantadosi, J. D. Fisher, and S. Grossman, Practical implementation of a modified continual reassessment method for dose-finding trials, Cancer Chemotherapy and Pharmacology, vol.41, issue.6, pp.429-465, 1998.
DOI : 10.1007/s002800050763

J. O. Quigley and E. Reiner, Miscellanea. A stopping rule for the continual reassessment method, Biometrika, vol.85, issue.3, pp.741-748, 1998.
DOI : 10.1093/biomet/85.3.741

J. M. Treluyer, S. Zohar, E. Rey, P. Hubert, F. Iserin et al., Minimum effective dose of midazolam for sedation of mechanically ventilated neonates, Journal of Clinical Pharmacy and Therapeutics, vol.15, issue.1, pp.479-485, 2005.
DOI : 10.1016/0140-6736(91)93235-2

M. De-spirlet, J. M. Treluyer, S. Chevret, E. Rey, M. Tournaire et al., Tocolytic effects of intravenous nitroglycerin. Fundamental and clinical pharmacology, pp.207-213, 2004.

L. Desfrere, S. Zohar, P. Morville, A. Brunhes, S. Chevret et al., Dose-finding study of ibuprofen in patent ductus arteriosus using the continual reassessment method, Journal of Clinical Pharmacy and Therapeutics, vol.55, issue.2, pp.121-132, 2005.
DOI : 10.1016/j.jpeds.2003.11.027

E. Fabre, S. Chevret, J. F. Piechaud, E. Rey, F. Vauzelle-kervoedan et al., An approach for dose finding of drugs in infants: sedation by midazolam studied using the continual reassessment method, British Journal of Clinical Pharmacology, vol.20, issue.4, pp.395-401, 1998.
DOI : 10.1046/j.1365-2125.1998.00788.x

F. Lefrere, S. Zohar, J. L. Bresson, S. Chevret, A. Mogenet et al., A double-blind low dose-finding phase II study of granulocyte colony-stimulating factor combined with chemotherapy for stem cell mobilization in patients with non-hodgkin's lymphoma, Haematologica, vol.91, pp.550-553, 2006.

J. B. Copas, Binary regression models for contaminated data, Journal of the royal statistical society. Series B, vol.50, pp.225-265, 1988.

G. B. Wetherill and K. D. Glazebrook, Sequential methods in statistics, 1986.

V. and R. Joseph, Efficient Robbins-Monro procedure for binary data, Biometrika, vol.91, issue.2, pp.461-470, 2004.
DOI : 10.1093/biomet/91.2.461

F. Hu, The asymptotic properties of the maximum-relevance weighted likelihood estimators, Canadian Journal of Statistics, vol.20, issue.1
DOI : 10.2307/3315356

F. Hu and J. V. Zidek, The weighted likelihood. The Canadian journal of statistics, pp.347-371, 2002.

F. Hu and W. F. Rosenberger, The Theory of Response-Adaptive Randomization in Clinical Trials Wiley series in probability and statistics, pp.107-119, 2006.

Y. K. Cheung and R. Chappell, Sequential Designs for Phase I Clinical Trials with Late-Onset Toxicities, Biometrics, vol.14, issue.Suppl., pp.1177-82, 2000.
DOI : 10.1111/j.0006-341X.2000.01177.x

J. O. Quigley, Retrospective Analysis of Sequential Dose-Finding Designs, Biometrics, vol.80, issue.3, pp.749-756, 2005.
DOI : 10.1111/j.1541-0420.2005.00353.x