.. Fréquence-du-choix-d, une copule spécifique pour chaque PGD pour 500 itérations avec les trois copules principales et trois copules intermédiaires. La copule choisie est en caractère foncé, p.53

L. Comparaison-de, AIC entre la méthode basée sur les copules et la régression logistique pour estimer la probabilité conditionnelle de T sachant Z ; 500 réplications effectuées

L. Comparaison-de, AIC entre la méthode basée sur les copules et la régression logistique dans le cas d'une mauvaise spécification de la copule

N. Fisher and . Copulas, Encyclopedia of statistical sciences, 1997.

A. Sklar, Fonctions de répartition à n dimensions et leurs marges, Publ Inst Statist Univ Paris, vol.8, issue.2, pp.229-231, 1959.

M. Ali, N. Mikhail, and M. Haq, A class of bivariate distributions including the bivariate logistic, Journal of Multivariate Analysis, vol.8, issue.3, pp.405-412, 1978.
DOI : 10.1016/0047-259X(78)90063-5

E. Gumbel, Bivariate Logistic Distributions, Journal of the American Statistical Association, vol.22, issue.3, pp.335-349, 1961.
DOI : 10.2307/3001655

R. Muirhead, . John, . Sons, T. Inc, N. Ave et al., Aspects of multivariate statistical analysis, pp.656-679, 1982.

C. Genest and J. Mackay, The joy of copulas : bivariate distributions with uniform marginals. The American Statistician, pp.280-283, 1986.

C. Savu and M. Trede, Hierarchical Archimedean Copulas : International Conference on High Frequency Finance, p.27, 2006.

M. Kendall, A NEW MEASURE OF RANK CORRELATION, Biometrika, vol.30, issue.1-2, pp.81-93, 1938.
DOI : 10.1093/biomet/30.1-2.81

J. Durbin and A. Stuart, Inversions and rank correlation coefficients, Journal of the Royal Statistical Society Series B (Methodological), pp.303-309, 1951.

W. Kruskal, Ordinal Measures of Association, Journal of the American Statistical Association, vol.2, issue.2, pp.814-861, 1958.
DOI : 10.2307/1412159

R. Nelsen, An introduction to copulas Springer Series in Statistics, pp.32-77, 2006.

A. Willan and D. Lin, Incremental net benefit in randomized clinical trials, Statistics in Medicine, vol.11, issue.11, pp.1563-1574, 2001.
DOI : 10.1002/hec.4730040503

A. Willan and B. O-'brien, Confidence intervals for cost-effectiveness ratios: An application of Fieller's theorem, Health Economics, vol.5, issue.4, pp.297-305, 1996.
DOI : 10.1002/(SICI)1099-1050(199607)5:4<297::AID-HEC216>3.0.CO;2-T

E. Kaplan and P. Meier, Nonparametric Estimation from Incomplete Observations, Journal of the American Statistical Association, vol.37, issue.282, pp.457-481, 1958.
DOI : 10.1214/aoms/1177731566

A. Willan, E. Chen, R. Cook, and D. Lin, Incremental net benefit in randomized clinical trials with quality-adjusted survival, Statistics in Medicine, vol.103, issue.3, pp.353-362, 2003.
DOI : 10.1161/01.CIR.103.10.1416

A. Willan, D. Lin, and A. Manca, Regression methods for cost-effectiveness analysis with censored data, Statistics in Medicine, vol.328, issue.1, pp.131-145, 2005.
DOI : 10.1007/978-1-4757-1229-2_14

E. Pullenayegum and A. Willan, Semi-parametric regression models for cost-effectiveness analysis: improving the efficiency of estimation from censored data, Statistics in Medicine, vol.57, issue.17, pp.3274-3299, 2007.
DOI : 10.1007/978-1-4757-1229-2_14

S. Thompson and R. Nixon, How sensitive are cost-effectiveness analyses to choice of parametric distributions ? Medical Decision Making, pp.416-423, 2007.

J. Stamey, D. Beavers, D. Faries, K. Price, and J. Seaman, Bayesian modeling of cost-effectiveness studies with unmeasured confounding: a simulation study, Pharmaceutical Statistics, vol.17, issue.6, pp.94-100, 2014.
DOI : 10.1002/pst.1604

D. Lin, Linear regression analysis of censored medical costs, Biostatistics, vol.1, issue.1, pp.35-47, 2000.
DOI : 10.1093/biostatistics/1.1.35

J. Buckley and I. James, Linear regression with censored data, Biometrika, vol.66, issue.3, pp.429-436, 1979.
DOI : 10.1093/biomet/66.3.429

R. Miller and J. Halpern, Regression with censored data, Biometrika, vol.69, issue.3, pp.521-531, 1982.
DOI : 10.1093/biomet/69.3.521

D. Oakes, A Concordance Test for Independence in the Presence of Censoring, Biometrics, vol.38, issue.2, pp.451-455, 1982.
DOI : 10.2307/2530458

D. Oakes, On consistency of Kendall's tau under censoring, Biometrika, vol.95, issue.4, pp.997-1001, 2008.
DOI : 10.1093/biomet/asn037

C. Genest and L. Rivest, Statistical Inference Procedures for Bivariate Archimedean Copulas, Journal of the American Statistical Association, vol.58, issue.423, pp.1034-1043, 1993.
DOI : 10.1214/aos/1176344685

L. Lakhal-chaieb, Copula inference under censoring, Biometrika, vol.97, issue.2, pp.505-512, 2010.
DOI : 10.1093/biomet/asq011

D. Silva, R. , F. Lopes, and H. , Copula, marginal distributions and model selection: a?Bayesian?note, Statistics and Computing, vol.64, issue.2, pp.313-320, 2008.
DOI : 10.1201/b13150

C. Genest, J. Neslehova, B. Ghorbal, and N. , ESTIMATORS BASED ON KENDALL'S TAU IN MULTIVARIATE COPULA MODELS, Australian & New Zealand Journal of Statistics, vol.40, issue.2, pp.157-177, 2011.
DOI : 10.1111/j.1467-842X.2011.00622.x

E. Maache, H. Lepage, and Y. , Spearman's rho and Kendall's tau for multivariate data sets, Mathematical Statistics and Applications Lecture Notes-Monograph Series, vol.42, pp.113-130, 2003.

E. Fieller, Some problems in interval estimation, Journal of the Statistical Royal Society, Series B, vol.16, pp.175-185, 1954.

N. Chaudhary and S. Stearns, ESTIMATING CONFIDENCE INTERVALS FOR COST-EFFECTIVENESS RATIOS: AN EXAMPLE FROM A RANDOMIZED TRIAL, Statistics in Medicine, vol.15, issue.13, pp.1447-1458, 1996.
DOI : 10.1002/(SICI)1097-0258(19960715)15:13<1447::AID-SIM267>3.0.CO;2-V

C. Siani and J. Moatti, Quelles méthodes de calcul des régions de confiance du ratio coût-efficacité incrémental choisir ? Universites d'Aix-Marseille II et III, p.46, 2002.

R. Nixon and S. Thompson, Methods for incorporating covariate adjustment, subgroup analysis and between-centre differences into cost-effectiveness evaluations. Health economics, pp.1217-1229, 2005.
DOI : 10.1002/hec.1008

K. Tsai and K. Peace, Analysis of Subgroup Data of Clinical Trials, Journal of Causal Inference, vol.1, issue.2, p.46, 2009.
DOI : 10.1515/jci-2012-0008

A. Vickers, R. Rees, C. Zollman, R. Mccarney, C. Smith et al., Acupuncture for chronic headache in primary care: large, pragmatic, randomised trial, BMJ, vol.328, issue.7442, pp.744-52, 2004.
DOI : 10.1136/bmj.38029.421863.EB

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC381326

D. Wonderling, A. Vickers, R. Grieve, and R. Mccarney, Cost effectiveness analysis of a randomised trial of acupuncture for chronic headache in primary care, BMJ, vol.328, issue.7442, pp.747-749, 2004.
DOI : 10.1136/bmj.38033.896505.EB

A. Vickers, Whose data set is it anyway? Sharing raw data from randomized trials, Trials, vol.7, issue.1, pp.15-52, 2006.
DOI : 10.1186/1745-6215-7-6

URL : http://doi.org/10.1186/1745-6215-7-15

J. Berkson, Application of the logistic function to bio-assay, Journal of the American Statistical Association, vol.39, issue.227, pp.357-365, 1944.

G. Barnard, Statistical Inference, Journal of the Royal Statistical Society, Series B, vol.11, issue.2, pp.115-149, 1949.
DOI : 10.1007/978-1-4613-8505-9_38

M. Guerriere and A. Detsky, Neural networks : what are they ? Annals of internal medicine, pp.906-907, 1991.

G. Hinton, How Neural Networks Learn from Experience, Scientific American, vol.267, issue.3, pp.145-151, 1992.
DOI : 10.1038/scientificamerican0992-144

C. Genest and J. Neslehova, A Primer on Copulas for Count Data, ASTIN Bulletin, vol.13, issue.02, pp.475-515, 200711.
DOI : 10.1016/j.csda.2006.10.009

M. Denuit and P. Lambert, Constraints on concordance measures in bivariate discrete data, Journal of Multivariate Analysis, vol.93, issue.1, pp.40-57, 2005.
DOI : 10.1016/j.jmva.2004.01.004

URL : http://doi.org/10.1016/j.jmva.2004.01.004

A. Marshall, Copulas, marginals, and joint distributions. Lecture Notes-Monograph Series, pp.213-222, 1996.
DOI : 10.1214/lnms/1215452620

P. Rosenbaum and D. Rubin, The central role of the propensity score in observational studies for causal effects, Biometrika, vol.70, issue.1, pp.41-55, 1983.
DOI : 10.1093/biomet/70.1.41

S. Guo and M. Fraser, Propensity score analysis : statistical methods and applications -2nd edition Advanced Quantitative Techniques in the Social Sciences Series. Thousand Oaks, California : SAGE ; 2015

T. Amemiya, Advanced econometrics, 1985.

Y. Tu, M. Kellett, V. Clerehugh, and M. Gilthorpe, Problems of correlations between explanatory variables in multiple regression analyses in the dental literature, British Dental Journal, vol.12, issue.7, pp.457-461, 2005.
DOI : 10.2307/1267352

J. Miles and M. Shevlin, Applying regression and correlation : A guide for students and researchers. Sage, 2001.

S. Glantz and B. Slinker, Multicollinearity and what to do about it Primer of Applied Regression & Analysis of Variance, pp.185-187, 2001.

E. Pedhazur, Multiple regression in behavioral research : Explanation and prediction, Coaching Eiticacy and Youth Sport, p.72, 1997.

P. Austin, P. Grootendrost, and G. Anderson, A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study, Statistics in Medicine, vol.2, issue.4, pp.734-753, 2007.
DOI : 10.1007/978-1-4757-2443-1

F. Vandenhende and P. Lambert, Improved rank-based dependence measures for categorical data, Statistics & Probability Letters, vol.63, issue.2, pp.157-163, 2003.
DOI : 10.1016/S0167-7152(03)00063-4

M. Scarsini, On measures of concordance. Stochastica : revista de matematica pura y aplicada, pp.201-218, 1984.
URL : https://hal.archives-ouvertes.fr/hal-00542380

I. Gijbels and J. Mielniczuk, Estimating the density of a copula function, Communications in Statistics - Theory and Methods, vol.4, issue.2, pp.445-464, 1990.
DOI : 10.1214/aop/1176993915

T. Bouezmarni, M. Mesfioui, and A. Tajar, On concordance measures for discrete data and dependance properties of Poisson model, Journal of Probability and Statistics.Article ID, vol.895742, issue.2, pp.1-15, 2009.

I. Gijbels and D. Sznajder, Positive quadrant dependence testing and constrained copula estimation, Canadian Journal of Statistics, vol.21, issue.4, pp.36-64, 2013.
DOI : 10.1002/cjs.11146

T. Yanagimoto and M. Okamoto, Partial orderings of permutations and monotonicity of a rank correlation statistic, Annals of the Institute of Statistical Mathematics, vol.39, issue.1, pp.489-506, 1969.
DOI : 10.1007/BF02532273

A. Tchen, Inequalities for distributions with given marginals. The Annals of Probability, pp.814-827, 1980.
DOI : 10.1214/aop/1176994668

V. Durrleman, A. Nikeghbali, and T. Roncalli, Which Copula is the Right One?, SSRN Electronic Journal, p.83, 2000.
DOI : 10.2139/ssrn.1032545

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.432.4002

P. Deheuvels, La fonction de dépendance empirique et ses propriétés Un test non paramétrique d'indépendance. Bulletin de la Classe des Sciences de l'Académie Royale de Belgique, pp.65-83, 1979.

H. Joe and . Multivariate-concordance, Multivariate concordance, Journal of Multivariate Analysis, vol.35, issue.1, pp.12-30, 1990.
DOI : 10.1016/0047-259X(90)90013-8

URL : http://doi.org/10.1016/0047-259x(90)90013-8

C. Genest, K. Ghoudi, and L. Rivest, A semiparametric estimation procedure of dependence parameters in multivariate families of distributions, Biometrika, vol.82, issue.3, pp.543-552, 1995.
DOI : 10.1093/biomet/82.3.543

S. Setoguchi, S. Schneeweiss, M. Brookhart, R. Glynn, and E. Cook, Evaluating uses of data mining techniques in propensity score estimation: a simulation study, Pharmacoepidemiology and Drug Safety, vol.49, issue.6, pp.546-555, 2008.
DOI : 10.1016/S0140-6736(05)60602-2

A. Panagiotelis, C. Czado, and H. Joe, Pair Copula Constructions for Multivariate Discrete Data, Journal of the American Statistical Association, vol.33, issue.499, pp.1063-1072, 2012.
DOI : 10.1080/01621459.2012.682850

R. Miller, Least squares regression with censored data, Biometrika, vol.63, issue.3, pp.449-464, 1976.
DOI : 10.1093/biomet/63.3.449

H. Koul, V. Susarla, and J. Van-ryzin, Regression Analysis with Randomly Right-Censored Data, The Annals of Statistics, vol.9, issue.6, pp.1276-1288, 1981.
DOI : 10.1214/aos/1176345644

K. Doksum, B. Yandell, K. Bickel, J. Doksum, and . Hodges-jr, Properties of regression estimates based on censored survival data, pp.140-156, 1983.

Z. Zheng, Regression analysis with censored data. PhD Dissertation, 1984.

Z. Zheng, A class of estimators of the parameters in linear regression with censored data, Acta Mathematicae Applicatae Sinica, vol.10, issue.3, pp.231-241, 1987.
DOI : 10.1007/BF02007667

S. Leurgans, Linear Models, Random Censoring and Synthetic Data, Biometrika, vol.74, issue.2, pp.301-309, 1987.
DOI : 10.2307/2336144

M. Zhou, Asymptotic Normality of the `Synthetic Data' Regression Estimator for Censored Survival Data, The Annals of Statistics, vol.20, issue.2, pp.1002-1021, 1992.
DOI : 10.1214/aos/1176348667

C. Srinivasan and M. Zhou, Linear Regression with Censoring, Journal of Multivariate Analysis, vol.49, issue.2, pp.179-201, 1994.
DOI : 10.1006/jmva.1994.1021

URL : http://doi.org/10.1006/jmva.1994.1021

J. Fan and I. Gijbels, Censored Regression: Local Linear Approximations and their Applications, Journal of the American Statistical Association, vol.8, issue.426, pp.560-570, 1994.
DOI : 10.1007/BF02007667

H. Noh, E. Ghouch, A. Bouezmarni, and T. , Copula-Based Regression Estimation and Inference, Journal of the American Statistical Association, vol.50, issue.502, pp.676-688, 2013.
DOI : 10.1080/01621459.2013.783842

J. Shih and T. Louis, Inferences on the Association Parameter in Copula Models for Bivariate Survival Data, Biometrics, vol.51, issue.4, pp.1384-1399, 1995.
DOI : 10.2307/2533269

H. Joe and J. Xu, The estimation method of inference functions for margins for multivariate models Technical Reports of Department of Statistics of University of British-Columbia, pp.1-21, 1996.

S. Lo, Y. Mack, and J. Wang, Density and hazard rate estimation for censored data via strong reptresentation of the Kaplan-Meier estimator. Probability Theory and Relatd Fields, pp.473-473, 1989.

M. Donsker, Justification and extension of Doob's heuristic approach to the Kolmogorov- Smirnov theorems. The Annals of mathematical statistics, pp.277-281, 1952.

S. Lo and K. Singh, The Product-Limit Estimator and the Bootstrap : Some Asymptotic Representations . Probability Theory and Related Fields, pp.455-465, 1985.
DOI : 10.1007/bf01000216

H. Dette, R. Van-hecke, and S. Volgushev, Some Comments on Copula-Based Regression, Journal of the American Statistical Association, vol.8, issue.507, pp.1319-1324, 2014.
DOI : 10.1002/cjs.5540330304

C. Genest, B. Rémillard, and D. Beaudoin, Goodness-of-fit tests for copulas: A review and a power study, Insurance: Mathematics and Economics, vol.44, issue.2, pp.199-213, 2009.
DOI : 10.1016/j.insmatheco.2007.10.005

J. Shih, A goodness-of-fit test for association in a bivariate survival model, Biometrika, vol.85, issue.1, pp.189-200, 1998.
DOI : 10.1093/biomet/85.1.189

D. Glidden, Checking the adequacy of the gamma frailty model for multivariate failure times, Biometrika, vol.86, issue.2, pp.381-393, 1999.
DOI : 10.1093/biomet/86.2.381