P. Rosenbaum and D. Rubin, The central role of the propensity score in observational studies for causal effects, Biometrika, vol.70, issue.1, pp.41-45, 1983.
DOI : 10.1093/biomet/70.1.41

P. Rosenbaum and D. Rubin, Reducing Bias in Observational Studies Using Subclassification on the Propensity Score, Journal of the American Statistical Association, vol.6, issue.387, pp.516-524, 1984.
DOI : 10.1080/01621459.1984.10478078

D. Agostino and R. Jr, Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group, pp.2265-2281, 1998.

P. Austin, A critical appraisal of propensity-score matching in the medical literature between, pp.2037-2049, 1996.

P. Rosenbaum, Model-Based Direct Adjustment, Journal of the American Statistical Association, vol.146, issue.398, p.387, 1987.
DOI : 10.1080/01621459.1987.10478441

J. Robins, M. Hernan, and B. Brumback, Marginal Structural Models and Causal Inference in Epidemiology, Epidemiology, vol.11, issue.5, pp.550-560, 2000.
DOI : 10.1097/00001648-200009000-00011

M. Joffe, T. Have, T. Feldman, H. Kimmel, and S. , Model Selection, Confounder Control, and Marginal Structural Models, The American Statistician, vol.58, issue.4, pp.272-279, 2004.
DOI : 10.1198/000313004X5824

J. Lunceford and M. Davidian, Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study, Statistics in Medicine, vol.23, issue.19, pp.2937-2960, 2004.
DOI : 10.1002/sim.7231

T. Kurth, A. Walker, R. Glynn, K. Chan, J. Gaziano et al., Results of Multivariable Logistic Regression, Propensity Matching, Propensity Adjustment, and Propensity-based Weighting under Conditions of Nonuniform Effect, American Journal of Epidemiology, vol.163, issue.3, pp.262-270, 2006.
DOI : 10.1093/aje/kwj047

P. Austin, The Relative Ability of Different Propensity Score Methods to Balance Measured Covariates Between Treated and Untreated Subjects in Observational Studies, Medical Decision Making, vol.29, issue.6, pp.661-677, 2009.
DOI : 10.1177/0272989X09341755

D. Rubin, Estimating Causal Effects from Large Data Sets Using Propensity Scores, Annals of Internal Medicine, vol.127, issue.8_Part_2, pp.757-763, 1997.
DOI : 10.7326/0003-4819-127-8_Part_2-199710151-00064

D. Wijeysundera, W. Beattie, P. Austin, J. Hux, and A. Laupacis, Epidural anaesthesia and survival after intermediate-to-high risk non-cardiac surgery: a population-based cohort study, The Lancet, vol.372, issue.9638, pp.372562-569, 2008.
DOI : 10.1016/S0140-6736(08)61121-6

D. Park, K. Seung, Y. Kim, J. Lee, W. Kim et al., Long-Term Safety and Efficacy of Stenting Versus Coronary Artery Bypass Grafting for Unprotected Left Main Coronary Artery Disease, Journal of the American College of Cardiology, vol.56, issue.2, pp.117-124, 2010.
DOI : 10.1016/j.jacc.2010.04.004

A. Fernandez-nebro, A. Olive, M. Castro, A. Varela, E. Riera et al., R: Long-term TNF-alpha blockade in patients with amyloid A amyloidosis complicating rheumatic diseases

L. Karlin, B. Arnulf, S. Chevret, L. Ades, R. M. et al., Tandem autologous non-myeloablative allogeneic transplantation in patients with multiple myeloma relapsing after a first high dose therapy, Bone Marrow Transplantation, vol.88, issue.2, pp.250-256, 2011.
DOI : 10.1038/leu.2008.88

G. Iapichino, D. Corbella, C. Minelli, G. Mills, A. Artigas et al., Reasons for refusal of admission to intensive care and impact on mortality, Intensive Care Medicine, vol.10, issue.10, pp.361772-1779, 2010.
DOI : 10.1007/s00134-010-1933-2

D. Rubin and N. Thomas, Matching Using Estimated Propensity Scores: Relating Theory to Practice, Biometrics, vol.52, issue.1, pp.249-264, 1996.
DOI : 10.2307/2533160

S. Perkins, W. Tu, M. Underhill, X. Zhou, and M. Murray, The use of propensity scores in pharmacoepidemiologic research, Pharmacoepidemiology and Drug Safety, vol.54, issue.2, pp.93-101, 2000.
DOI : 10.1002/(SICI)1099-1557(200003/04)9:2<93::AID-PDS474>3.0.CO;2-I

P. Austin, Goodness-of-fit diagnostics for the propensity score model when estimating treatment effects using covariate adjustment with the propensity score, Pharmacoepidemiology and Drug Safety, vol.13, issue.12, pp.1202-1217, 2008.
DOI : 10.1002/pds.1673

S. Weitzen, K. Lapane, A. Toledano, A. Hume, and V. Mor, Weaknesses of goodness-of-fit tests for evaluating propensity score models: the case of the omitted confounder, Pharmacoepidemiology and Drug Safety, vol.14, issue.4, pp.227-238, 2005.
DOI : 10.1002/pds.986

P. Austin, The performance of different propensity score methods for estimating marginal odds ratios, Statistics in Medicine, vol.24, issue.16, pp.3078-3094, 2007.
DOI : 10.1002/sim.2781

A. Forbes and S. Shortreed, Inverse probability weighted estimation of the marginal odds ratio: correspondence regarding 'The performance of different propensity score methods for estimating marginal odds ratios

P. Austin, Some Methods of Propensity-Score Matching had Superior Performance to Others: Results of an Empirical Investigation and Monte Carlo simulations, Biometrical Journal, vol.31, issue.S1, pp.171-184, 2009.
DOI : 10.1002/bimj.200810488

. Rosenbaum, P: Observational Studies.2 nd Edition, 2002.

M. Brookhart, S. Schneeweiss, K. Rothman, R. Glynn, J. Avorn et al., Variable Selection for Propensity Score Models, American Journal of Epidemiology, vol.163, issue.12, pp.1149-1156, 2006.
DOI : 10.1093/aje/kwj149

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1513192

S. Cole and M. Hernan, Constructing Inverse Probability Weights for Marginal Structural Models, American Journal of Epidemiology, vol.168, issue.6, pp.656-664, 2008.
DOI : 10.1093/aje/kwn164

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2732954

A. Leon and D. Hedeker, Quantile Stratification Based on a Misspecified Propensity Score in Longitudinal Treatment Effectiveness Analyses of Ordinal Doses, Comput Stat Data Anal, issue.12, pp.516114-6122, 2007.

P. Austin, P. Grootendorst, 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 : 10.1002/sim.2580

P. Austin, Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples, Statistics in Medicine, vol.17, issue.1, pp.3083-3107, 2009.
DOI : 10.1002/sim.3697

B. Hansen, The essential role of balance tests in propensity-matched observational studies: Comments on ???A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003??? by Peter Austin,Statistics in Medicine, Statistics in Medicine, vol.15, issue.12, pp.2050-2054, 1996.
DOI : 10.1002/sim.3208

E. Gayat, R. Pirracchio, M. Resche-rigon, A. Mebazaa, J. Mary et al., Propensity scores in intensive care and anaesthesiology literature: a systematic review, Intensive Care Medicine, vol.24, issue.12, pp.1993-2003, 2010.
DOI : 10.1007/s00134-010-1991-5

P. Austin, Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies, Pharmaceutical Statistics, vol.29, issue.1, pp.150-161, 2011.
DOI : 10.1002/pst.433

R. Dehija and S. Wahba, Propensity Score-Matching Methods for Nonexperimental Causal Studies, Review of Economics and Statistics, vol.76, issue.4, pp.151-161, 2002.
DOI : 10.2307/2289065

M. Frölich, Finite-Sample Properties of Propensity-Score Matching and Weighting Estimators, Review of Economics and Statistics, vol.86, issue.1, pp.77-90, 2004.
DOI : 10.2307/1390658

P. Austin, P. Grootendorst, S. Normand, and G. Anderson, Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study, Statistics in Medicine, vol.137, issue.4, pp.754-768, 2007.
DOI : 10.1002/sim.2618

E. Martens, W. Pestman, O. Klungel, T. Sharon-lise, G. M. Normand et al., Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: A Monte Carlo study (p n/a) by Peter C. Austin, Paul Grootendorst, Sharon-Lise T. Normand, Geoffrey M. Anderson,Statistics in Medicine, Published Online: 16 June 2006. DOI: 10.1002/sim.2618, Statistics in Medicine, vol.58, issue.16, pp.3208-3210, 2006.
DOI : 10.1002/sim.2878

S. Stampf, E. Graf, C. Schmoor, and M. Schumacher, Estimators and confidence intervals for the marginal odds ratio using logistic regression and propensity score stratification, Statistics in Medicine, vol.5, issue.4, pp.760-769, 2010.
DOI : 10.1002/sim.3811

M. Gail, S. Wieand, and S. Piantadosi, Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates, Biometrika, vol.71, issue.3, pp.431-444, 1984.
DOI : 10.1093/biomet/71.3.431

S. Greenland, INTERPRETATION AND CHOICE OF EFFECT MEASURES IN EPIDEMIOLOGIC ANALYSES1, American Journal of Epidemiology, vol.125, issue.5, pp.761-768, 1987.
DOI : 10.1093/oxfordjournals.aje.a114593