W. Zarin, A. A. Veroniki, V. Nincic, A. Vafaei, E. Reynen et al., Characteristics and knowledge synthesis approach for 456 network meta-analyses: a scoping review, BMC Med, vol.15, p.28052774, 2017.

G. H. Guyatt, A. D. Oxman, G. E. Vist, R. Kunz, Y. Falck-ytter et al., GRADE: an emerging consensus on rating quality of evidence and strength of recommendations, BMJ, vol.336, pp.924-930, 2008.

G. Guyatt, A. D. Oxman, E. A. Akl, R. Kunz, G. Vist et al., GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables, J Clin Epidemiol, vol.64, pp.383-94, 2011.

M. A. Puhan, H. J. Schü-nemann, M. H. Murad, T. Li, R. Brignardello-petersen et al., A GRADE Working Group approach for rating the quality of treatment effect estimates from network meta-analysis, BMJ, vol.349, p.5630, 2014.

G. Salanti, D. Giovane, C. Chaimani, A. Caldwell, D. M. Higgins et al., Evaluating the quality of evidence from a network meta-analysis, PLoS ONE, vol.9, issue.7, p.99682, 2014.

J. P. Jansen, T. Trikalinos, J. C. Cappelleri, J. Daw, S. Andes et al., Indirect treatment comparison/network meta-analysis study questionnaire to assess relevance and credibility to inform health care decision making: an ISPOR-AMCP-NPC Good Practice Task Force report, Value Health, vol.17, pp.157-73, 2014.

D. C. Hoaglin, N. Hawkins, J. P. Jansen, D. A. Scott, R. Itzler et al., Conducting indirect-treatment-comparison and network-meta-analysis studies: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 2. Value Health, vol.14, pp.429-466, 2011.

A. E. Ades, D. M. Caldwell, S. Reken, N. J. Welton, A. J. Sutton et al., Evidence synthesis for decision making 7: a reviewer's checklist, Med Decis Making, vol.33, pp.679-91, 2013.

S. Dias, A. J. Sutton, A. E. Ades, and N. J. Welton, Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials, Med Decis Making, vol.33, pp.607-624, 2013.

G. C. Siontis, D. Mavridis, J. P. Greenwood, B. Coles, A. Nikolakopoulou et al., Outcomes of non-invasive diagnostic modalities for the detection of coronary artery disease: network meta-analysis of diagnostic randomised controlled trials, BMJ, vol.360, p.504, 2018.

H. Naci, J. Brugts, and T. Ades, Comparative tolerability and harms of individual statins: a study-level network meta-analysis of 246 955 participants from 135 randomized, controlled trials, Circ Cardiovasc Qual Outcomes, vol.6, pp.390-399, 2013.

A. Cipriani, T. A. Furukawa, G. Salanti, A. Chaimani, L. Z. Atkinson et al., Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis, Lancet, vol.391, issue.17, p.29477251, 2018.

, Bern: Institute of Social and Preventive Medicine, CINeMA: Confidence in Network Meta-Analysis, 2017.

G. Rücker, U. Krahn, J. König, O. Efthimiou, and G. Schwarzer, netmeta: network meta-analysis using frequentist methods

T. Papakonstantinou, flow_contribution: R package to calculate contribution of studies in network metaanalysis. GitHub, 2020.

J. C. Dumville, M. O. Soares, S. O'meara, and N. Cullum, Systematic review and mixed treatment comparison: dressings to heal diabetic foot ulcers, Diabetologia, vol.55, p.22544222, 2012.

B. Hutton, G. Salanti, D. M. Caldwell, A. Chaimani, C. H. Schmid et al., The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations, Ann Intern Med, vol.162, pp.777-84, 2015.

J. Higgins, D. G. Altman, P. C. Gøtzsche, P. Jü-ni, D. Moher et al., The Cochrane Collaboration's tool for assessing risk of bias in randomised trials, BMJ, vol.343, 2011.

J. Sterne, J. Savovi?, M. J. Page, R. G. Elbers, N. S. Blencowe et al., RoB 2: a revised tool for assessing risk of bias in randomised trials, BMJ, vol.366, p.4898, 2019.

G. H. Guyatt, A. D. Oxman, G. Vist, R. Kunz, J. Brozek et al., GRADE guidelines: 4. Rating the quality of evidence-study limitations (risk of bias), J Clin Epidemiol, vol.64, pp.407-422, 2011.

R. Brignardello-petersen, A. Bonner, P. E. Alexander, R. A. Siemieniuk, T. A. Furukawa et al., Advances in the GRADE approach to rate the certainty in estimates from a network meta-analysis, J Clin Epidemiol, vol.93, pp.36-44, 2018.

T. Papakonstantinou, A. Nikolakopoulou, G. Rücker, A. Chaimani, G. Schwarzer et al., Estimating the contribution of studies in network meta-analysis: paths, flows and streams, vol.7, p.610, 2018.

M. J. Page, J. E. Mckenzie, J. Kirkham, K. Dwan, S. Kramer et al., Bias due to selective inclusion and reporting of outcomes and analyses in systematic reviews of randomised trials of healthcare interventions, Cochrane Database Syst Rev, issue.10, p.35, 2014.

K. Dwan, D. G. Altman, J. A. Arnaiz, J. Bloom, A. Chan et al., Systematic review of the empirical evidence of study publication bias and outcome reporting bias, PLoS ONE, vol.3, issue.8, p.3081, 2008.

K. Dwan, C. Gamble, P. R. Williamson, and J. J. Kirkham, Systematic review of the empirical evidence of study publication bias and outcome reporting bias-an updated review, PLoS ONE, vol.8, issue.7, p.66844, 2013.

R. W. Scherer, P. Langenberg, and E. Von-elm, Full publication of results initially presented in abstracts, Cochrane Database Syst Rev, issue.2, 2007.

E. Wager and P. Williams, Hardly worth the effort"? Medical journals' policies and their editors' and publishers' views on trial registration and publication bias: quantitative and qualitative study, BMJ, vol.347, 2013.

J. M. Stern and R. J. Simes, Publication bias: evidence of delayed publication in a cohort study of clinical research projects, BMJ, vol.315, pp.640-645, 1997.

K. Dickersin and I. Chalmers, Recognizing, investigating and dealing with incomplete and biased reporting of clinical research: from Francis Bacon to the WHO, J R Soc Med, vol.104, pp.532-540, 2011.

R. D. Riley, J. Higgins, and J. J. Deeks, Interpretation of random effects meta-analyses, BMJ, vol.342, issue.549, 2011.

R. M. Turner, J. Davey, M. J. Clarke, S. G. Thompson, and J. P. Higgins, Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews, Int J Epidemiol, vol.41, pp.818-845, 2012.

K. M. Rhodes, R. M. Turner, and J. Higgins, Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data, J Clin Epidemiol, vol.68, pp.52-60, 2015.

G. Lu and A. E. Ades, Assessing evidence inconsistency in mixed treatment comparisons, J Am Stat Assoc, vol.101, pp.447-59, 2006.

G. Lu and A. Ades, Modeling between-trial variance structure in mixed treatment comparisons, Biostatistics, vol.10, pp.792-805, 2009.

H. C. Bucher, G. H. Guyatt, L. E. Griffith, and S. D. Walter, The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials, J Clin Epidemiol, vol.50, issue.97, pp.49-57, 1997.

S. Dias, N. J. Welton, A. J. Sutton, D. M. Caldwell, G. Lu et al., NICE DSU technical support document 4: inconsistency in networks of evidence based on randomised controlled trials. London: National Institute for Health and Care Excellence, 2014.

A. A. Veroniki, D. Mavridis, J. P. Higgins, and G. Salanti, Characteristics of a loop of evidence that affect detection and estimation of inconsistency: a simulation study, BMC Med Res Methodol, vol.14, p.106, 2014.

F. Song, A. Clark, M. O. Bachmann, and J. Maas, Simulation evaluation of statistical properties of methods for indirect and mixed treatment comparisons, BMC Med Res Methodol, vol.12, p.138, 2012.

D. M. Phillippo, S. Dias, N. J. Welton, D. M. Caldwell, N. Taske et al., Threshold analysis as an alternative to GRADE for assessing confidence in guideline recommendations based on network meta-analyses

, Ann Intern Med, vol.170, issue.8, pp.538-584, 2019.

S. Kanters, N. Ford, E. Druyts, K. Thorlund, E. J. Mills et al., Use of network meta-analysis in clinical guidelines, Bull World Health Organ, vol.94, pp.782-786, 2016.

M. Petropoulou, A. Nikolakopoulou, A. Veroniki, P. Rios, A. Vafaei et al., Bibliographic study showed improving statistical methodology of network meta-analyses published between 1999 and 2015, J Clin Epidemiol, vol.82, pp.20-28, 2017.

T. Papakonstantinou, A. Nikolakopoulou, J. Higgins, M. Egger, and G. Salanti, CINeMA: software for semiautomated assessment of the confidence in the results of network meta-analysis, Campbell Syst Rev, vol.16, p.1080, 2020.