D. Murray, Design and Analysis of Group-Randomized Trials, 1998.

R. Hayes, L. Moulton, and N. Klar, Cluster Randomised Trials Boca Raton Design and Analysis of Cluster Randomization Trials in Health Research, 2009.

S. Eldridge and K. S. , A Practical Guide to Cluster Randomised Trials in Health Services Research
DOI : 10.1002/9781119966241

P. Li, D. Redden, M. Fay, and B. Graubard, Small sample performance of bias?corrected sandwich estimators for cluster? 40 Small?Sample Adjustments for Wald?Type Tests Using Sandwich 44. Pan W, Wall MM. Small-sample adjustments in using the sandwich variance estimator in 61, Khajeh-Kazemi R

P. Westgate and T. Braun, The effect of cluster size imbalance and covariates on the estimation 63

P. Westgate, A bias?corrected covariance estimate for improved inference with quadratic 64
DOI : 10.1002/sim.5479

P. Westgate, A covariance correction that accounts for correlation estimation to improve 65
DOI : 10.1080/00949655.2015.1089873
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5089177

P. Westgate, Criterion for the simultaneous selection of a working correlation structure and 24

F. Asgari, A. Biglarian, B. Seifi, A. Bakhshi, H. Miri et al., Using quadratic inference functions 557 to determine the factors associated with obesity: findings from the STEPS Survey in Iran

K. Yang, L. Tao, and G. Mahara, An association of platelet indices with blood pressure in Beijing 563 adults: Applying quadratic inference function for a longitudinal study, Medicine

S. Gruber and M. Van-der-laan, A targeted maximum likelihood estimator of a causal effect on a 568 bounded continuous outcome, Int J Biostat, vol.6, issue.1, pp.1-18, 2010.

P. Kotwani, L. Balzer, and D. Kwarisiima, Evaluating linkage to care for hypertension after community-based screening in rural Uganda, Tropical Medicine & International Health, vol.291, issue.4, pp.459-468, 2014.
DOI : 10.1001/jama.291.21.2616

J. Ahern, D. Karasek, A. Luedtke, T. Bruckner, and M. Van-der-laan, Racial/Ethnic Differences in the Role of Childhood Adversities for Mental Disorders Among a Nationally Representative Sample of Adolescents, Epidemiology, vol.27, issue.5, pp.697-704, 2016.
DOI : 10.1097/EDE.0000000000000507

L. Balzer, M. Petersen, M. Van-der-laan, E. Moodie, and R. Platt, Targeted estimation and inference for the sample average treatment effect in trials with and without pair-matching, 3732. 577 74. Schnitzer ME, van der Laan MJ, pp.3717-576, 2016.
DOI : 10.1002/sim.2731

M. Polley, E. Hubbard, and A. , Super learner, Stat Appl Genet Mol Biol, vol.6, issue.1, 2007.

M. Gail, S. Mark, R. Carroll, S. Green, and D. Pee, ON DESIGN CONSIDERATIONS AND RANDOMIZATION-BASED INFERENCE FOR COMMUNITY INTERVENTION TRIALS, Statistics in Medicine, vol.15, issue.11, pp.1069-1092, 1996.
DOI : 10.1002/(SICI)1097-0258(19960615)15:11<1069::AID-SIM220>3.0.CO;2-Q

K. Haines, T. Chilton, P. Girling, A. Lilford, and R. , The stepped wedge cluster randomised 584 trial: rationale, design, analysis, and reporting, BMJ, vol.350, issue.585, pp.391-78, 2015.

D. Spiegelman, Evaluating Public Health Interventions: 2. Stepping Up to Routine Public Health Evaluation With the Stepped Wedge Design, American Journal of Public Health, vol.106, issue.3, pp.453-457, 2016.
DOI : 10.2105/AJPH.2016.303068

C. Davey, J. Hargreaves, and J. Thompson, Analysis and reporting of stepped wedge randomised 588 controlled trials: synthesis and critical appraisal of published studies, Trials, vol.58916, issue.590, pp.358-80, 2010.

N. Mdege, M. Man, C. Taylor, D. Torgerson, A. Copas et al., Systematic review of stepped wedge cluster 81 Designing a stepped wedge 82 Leveraging contact network structure in the design 83. Ebola ça Suffit Ring Vaccination Trial Consortium. The ring vaccination trial: a novel cluster 84 The dynamic 25

A. Banerjee, A. Chandrasekhar, E. Duflo, and M. Jackson, The diffusion of microfinance, Science, vol.605341, issue.6144, 2013.

E. Ogburn and T. Vanderweele, Causal Diagrams for Interference, Statistical Science, vol.29, issue.4, pp.559-578, 2014.
DOI : 10.1214/14-STS501

T. Vanderweele, E. Tchetgen, and M. Halloran, Components of the Indirect Effect in Vaccine Trials, Epidemiology, vol.23, issue.5, pp.751-609, 2012.
DOI : 10.1097/EDE.0b013e31825fb7a0

S. Teerenstra, M. Moerbeek, R. Melis, and G. Borm, A comparison of methods to analyse continuous data from pseudo cluster randomized trials, Clustered Randomized Trials of Infectious Processes. arXiv preprint, pp.4100-4115, 2007.
DOI : 10.1002/sim.2851

S. Baldwin, D. Bauer, E. Stice, and P. Rohde, Evaluating models for partially clustered designs. 614 Psychological Methods, pp.149-165, 2011.
DOI : 10.1037/a0023464
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3987820

F. Satterthwaite, An Approximate Distribution of Estimates of Variance Components, Biometrics Bulletin, vol.2, issue.6, pp.110-114, 1946.
DOI : 10.2307/3002019

S. Murray, D. Alfano, C. Shadish, W. Hannan, P. Baker et al., Individually randomized 618 group treatment trials: a critical appraisal of frequently used design and analytic approaches

C. Roberts and S. Roberts, Design and analysis of clinical trials with clustering effects due to treatment, Clinical Trials, vol.2, issue.2, pp.152-162, 2005.
DOI : 10.1191/1740774505cn076oa

C. Roberts and R. Walwyn, Design and analysis of non-pharmacological treatment trials with multiple therapists per patient, Statistics in Medicine, vol.377, issue.9768, pp.81-98, 2013.
DOI : 10.1016/S0140-6736(11)60096-2

R. Andridge, A. Shoben, K. Muller, and D. Murray, Analytic methods for individually randomized 625 group treatment trials and group-randomized trials when subjects belong to multiple groups
DOI : 10.1002/sim.6083
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4013262

K. Díaz-ordaz, M. Kenward, A. Cohen, C. Coleman, and S. Eldridge, Are missing data adequately handled in cluster randomised trials? A systematic review and guidelines, Clinical Trials, vol.342, issue.4, pp.590-600, 2014.
DOI : 10.1136/bmj.d40

C. Desouza, A. Legedza, and A. Sankoh, An Overview of Practical Approaches for Handling Missing Data in Clinical Trials, Journal of Biopharmaceutical Statistics, vol.44, issue.6, pp.1055-1073, 2009.
DOI : 10.1080/10543400802071303

K. Diaz-ordaz and J. Bartlett, Missing continuous outcomes under covariate dependent 633 missingness in cluster randomised trials, Stat Methods Med Res, vol.634, p.99, 2016.

S. Seaman and I. White, Review of inverse probability weighting for dealing with missing data, Statistical Methods in Medical Research, vol.92, issue.3
DOI : 10.1093/biomet/63.3.581

S. Vansteelandt, A. Rotnitzky, and J. Robins, Estimation of Regression Models for the Mean of Repeated Outcomes Under Nonignorable Nonmonotone Nonresponse, Biometrika, vol.94, issue.4, pp.841-860, 2007.
DOI : 10.1093/biomet/asm070

L. Thabane, L. Mbuagbaw, and S. Zhang, A tutorial on sensitivity analyses in clinical trials: the 26

S. Seaman, J. Galati, D. Jackson, and C. J. , What is meant by " missing at random " ? Stat Sci, pp.257-268, 2013.
DOI : 10.1214/13-sts415
URL : http://arxiv.org/abs/1306.2812

S. Belitser, E. Martens, W. Pestman, R. Groenwold, A. Boer et al., Measuring balance 654 and model selection in propensity score methods, Pharmacoepidemiol Drug Saf, vol.65520, issue.11, pp.1115-1129, 2011.
DOI : 10.1002/pds.2188

M. Prague, R. Wang, D. Gruttola, and V. , CRTgeeDR: An R Package for Doubly Robust Generalized 657 Estimating Equations Estimations in Cluster Randomized Trials with Missing Data

M. Prague, R. Wang, A. Stephens, T. Tchetgen, E. Degruttola et al., Accounting for interactions and complex inter-subject dependency in estimating treatment effect in cluster-randomized trials with missing outcomes, Biometrics, vol.64, issue.4, pp.1066-1077, 2016.
DOI : 10.1111/j.1541-0420.2007.00976.x
URL : https://hal.archives-ouvertes.fr/hal-01579083

S. Seaman, I. White, A. Copas, and L. Li, Combining Multiple Imputation and Inverse-Probability Weighting, Biometrics, vol.30, issue.1, pp.129-137, 2012.
DOI : 10.1002/sim.4067
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3412287

B. Hansen and J. Bowers, Covariate Balance in Simple

C. Leyrat, A. Caille, Y. Foucher, and B. Giraudeau, Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic, BMC Medical Research Methodology, vol.38, issue.6, p.9, 2016.
DOI : 10.1080/03610910902859574

A. Leon, H. Demirtas, C. Li, and D. Hedeker, Subject-level matching for imbalance in cluster randomized trials with a small number of clusters, Pharmaceutical Statistics, vol.51, issue.5, pp.268-274, 2013.
DOI : 10.1016/j.csda.2006.12.021

M. Campbell, D. Elbourne, and D. Altman, CONSORT statement: extension to cluster randomised trials, BMJ, vol.328, issue.7441, pp.702-708, 2004.
DOI : 10.1136/bmj.328.7441.702
URL : http://www.bmj.com/content/bmj/328/7441/702.full.pdf

J. Hutton, Are distinctive ethical principles required for cluster randomized controlled trials?, p.673
DOI : 10.1002/1097-0258(20010215)20:3<473::aid-sim805>3.3.co;2-4

M. Taljaard, S. Chaudhry, and J. Brehaut, Survey of consent practices in cluster randomized trials: Improvements are needed in ethical conduct and reporting, Clinical Trials, vol.7, issue.7, pp.60-69, 2014.
DOI : 10.1371/journal.pone.0040436

J. Sim and A. Dawson, Informed Consent and Cluster-Randomized Trials, American Journal of Public Health, vol.102, issue.3, pp.480-485, 2012.
DOI : 10.2105/AJPH.2011.300389
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3487664

C. Weijer, J. Grimshaw, and M. Eccles, The Ottawa statement on the ethical design and 679 conduct of cluster randomized trials, PLoS Med, vol.9, issue.11, 2012.

R. Van-der-graaf, H. Koffijberg, and D. Grobbee, The ethics of cluster-randomized trials requires further evaluation: a refinement of the Ottawa Statement, Journal of Clinical Epidemiology, vol.68, issue.9, pp.1108-682, 2015.
DOI : 10.1016/j.jclinepi.2015.03.013

D. Zeng, D. Lin, and X. Lin, Semiparametric transformation models with random effects for clustered 684 failure time data, Stat Sin, vol.18, issue.1, pp.355-377, 2008.

T. Cai, S. Cheng, W. L. Caille, A. Kerry, S. Tavernier et al., Semiparametric mixed-effects models for clustered failure time data Timeline cluster: a graphical tool 700 to identify risk of bias in cluster randomised trials, J Am BMJ, vol.129354, 2016.

J. Ma, L. Thabane, and J. Kaczorowski, Comparison of Bayesian and classical methods in the 702 analysis of cluster randomized controlled trials with a binary outcome: the Community 703

R. Grieve, R. Nixon, and S. Thompson, Bayesian Hierarchical Models for Cost-Effectiveness Analyses that Use Data from Cluster Randomized Trials, Medical Decision Making, vol.52, issue.2, pp.163-175, 2010.
DOI : 10.1002/hec.1077

A. Clark and M. Bachmann, Bayesian methods of analysis for cluster randomized trials with count 707 outcome data, Stat Med, vol.29, issue.2, pp.199-209, 2010.

M. Gomes, E. Ng, R. Grieve, R. Nixon, J. Carpenter et al., Developing Appropriate Methods for Cost-Effectiveness Analysis of Cluster Randomized Trials, Medical Decision Making, vol.1, issue.2, pp.350-361, 2012.
DOI : 10.1201/9780203489437

K. Díaz-ordaz, M. Kenward, M. Gomes, and R. Grieve, Multiple imputation methods for bivariate outcomes in cluster randomised trials, Statistics in Medicine, vol.11, issue.4, pp.3482-3496, 2016.
DOI : 10.1177/1471082X1001100404

E. Ng, K. Diaz-ordaz, R. Grieve, R. Nixon, S. Thompson et al., Multilevel models for 714 cost-effectiveness analyses that use cluster randomised trial data: an approach to model choice

K. Díaz?ordaz, M. Kenward, and R. Grieve, Handling missing values in cost effectiveness analyses that use data from cluster randomized trials, Journal of the Royal Statistical Society: Series A (Statistics in Society), vol.54, issue.2, pp.457-474, 2014.
DOI : 10.1016/j.csda.2009.01.016

J. Hox, M. Moerbeek, A. Kluytmans, and R. Van-de-schoot, Analyzing indirect effects in cluster 719 randomized trials. The effect of estimation method, number of groups and group sizes on 720 accuracy and power, Front Psychol, vol.5, p.78, 2014.

D. Mackinnon, A. Fairchild, and M. Fritz, Mediation Analysis, Annual Review of Psychology, vol.58, issue.1, pp.593-614, 2007.
DOI : 10.1146/annurev.psych.58.110405.085542

T. Vanderweele, G. Hong, S. Jones, and J. Brown, Mediation and spillover effects in group- 723 randomized trials: a case study of the 4Rs educational intervention, J Am Stat Assoc, vol.724108, issue.502, pp.469-482, 2013.

T. Vanderweele, A Unification of Mediation and Interaction, Epidemiology, vol.25, issue.5, pp.749-761, 2014.
DOI : 10.1097/EDE.0000000000000121

J. Robins, Marginal structural models versus structural nested models as tools for causal 728 inference, Statistical models in epidemiology, the environment 729 and clinical trials, pp.95-134, 1999.
DOI : 10.1007/978-1-4612-1284-3_2

J. Robins, A. Rotnitzky, and L. Zhao, Estimation of Regression Coefficients When Some Regressors are not Always Observed, Journal of the American Statistical Association, vol.137, issue.2, pp.846-866, 1994.
DOI : 10.1002/sim.4780110608

E. De-hoop, S. Teerenstra, B. Van-gaal, M. Moerbeek, and G. Borm, The ???best balance??? allocation led to optimal balance in cluster-controlled trials, Journal of Clinical Epidemiology, vol.65, issue.2, pp.132-137, 2012.
DOI : 10.1016/j.jclinepi.2011.05.006