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Appendix K: Network meta-analysis reporting standards

Reporting results of network meta-analysis (NMA) should meet the criteria in the modified version of the PRISMA-NMA checklist specified below. The modified version of the checklist includes only a subset of items in the full checklist that are specifically applicable to reporting the results of network meta-analysis.

1. Describe the reasons for the evidence review in the context of what is already known, including why an NMA has been conducted.

2. Specify the study characteristics (for example, PICOs, length of follow-up) and report characteristics (for example, years considered, language, publication status) used to decide the eligibility of studies, giving the reasons for the characteristics used. Clearly describe eligible treatments included in the treatment network and note whether any have been clustered or merged into the same node (with justification).

3. Describe methods used to explore the geometry of the treatment network and potential biases related to it (for example, if there are important links in the network where the studies are at high-risk of bias). This should include how the evidence base has been graphically summarised for presentation, and what characteristics were compiled and used to describe the evidence base to readers.

4. State the principal summary measures (for example, risk ratio, difference in means). Also describe the use of additional summary measures assessed, such as treatment rankings and surface under the cumulative ranking curve (SUCRA) values, as well as modified approaches used to present summary findings from meta-analyses.

5. Describe the methods of handling data and combining results of studies for each NMA. This should include, but not be limited to:

a) handling of multi-arm trials

b) selection of variance structure

c) selection of prior distributions in Bayesian analyses

d) exam of model fit.

6. Describe the statistical methods used to evaluate the agreement of direct and indirect evidence in the treatment network(s) studied. Describe efforts taken to address inconsistency when found.

7. Describe methods of additional analyses if done, indicating which were pre-specified. This may include, but not be limited to, the following:

a) sensitivity or subgroup analyses

b) meta-regression analyses

c) alternative formulations of the treatment network

d) use of alternative prior distributions for Bayesian analyses (if applicable).

8. Provide a network graph of the included studies to enable visualisation of the geometry of the treatment network.

9. Provide a brief overview of characteristics of the treatment network. This may include commentary on the abundance of trials and randomised patients for the different interventions and pairwise comparisons in the network, gaps of evidence in the treatment network, and potential biases reflected by the network structure (for example, publication bias).

10. Present results of each meta-analysis done, including confidence/credible intervals. In larger networks, authors may focus on comparisons versus a particular comparator (for example, placebo or standard care). League tables and forest plots may be considered to summarise pairwise comparisons. If additional summary measures were explored (such as treatment rankings), these should also be presented.

11. Describe results from investigations of inconsistency. This may include such information as measures of model fit to compare consistency and inconsistency models, P values from statistical tests, or summary of inconsistency estimates from different parts of the treatment network.

12. Give results of additional analyses, if done (for example, sensitivity or subgroup analyses, meta-regression analyses, alternative network geometries studied, alternative choice of prior distributions for Bayesian analyses).

13. Discuss limitations at study and outcome level (for example, risk of bias), and at review level (for example, incomplete retrieval of identified research, reporting bias). Comment on the validity of the assumptions, such as transitivity and consistency. Comment on any concerns regarding network geometry (for example, avoidance of certain comparisons).

Network software

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MSc Social Network Analysis

The programme offers a comprehensive training in social network analysis, covering theories, methods and applications of social networks in social sciences. Students will learn the theoretical foundations of social network analysis, the constitutive elements of research design, techniques for data collection, advance methods for social network data analysis and visualization, statistical modelling of social networks and mixed methods.

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