Network meta-analysis

Network meta-analysis (NMA) is a statistical method used to compare multiple interventions simultaneously, even when head-to-head comparisons between all the interventions are not available. It allows for the integration of direct and indirect evidence from multiple randomized controlled trials (RCTs) to estimate the relative effectiveness of different interventions, including those that have not been compared directly in any single trial.

In NMA, the interventions of interest are linked together in a network of trials, where each trial compares two or more interventions. The data from these trials are then synthesized to produce a network of treatment comparisons, which can be used to estimate the relative effects of each intervention.

The main steps involved in performing a network meta-analysis are:

1. Systematic review of the literature: This involves identifying all relevant RCTs and collecting data on the outcomes of interest, as well as the characteristics of the trials and their populations.

2. Network formation: This involves constructing a network of trials that compares the interventions of interest. The trials are linked together based on the interventions that they have compared.

3. Statistical analysis: This involves using statistical models to estimate the relative effects of each intervention, as well as the uncertainty associated with these estimates. This can involve direct comparison of interventions, as well as indirect comparison through the network of trials.

4. Sensitivity analyses: These are performed to assess the robustness of the results to different assumptions and modeling choices.

5. Interpretation of results: This involves interpreting the estimates of treatment effects and the associated uncertainty, and communicating the findings in a clear and transparent way.

NMA has several advantages over traditional pairwise meta-analysis, including:

• It allows for the comparison of multiple interventions simultaneously, even when head-to-head comparisons between all the interventions are not available.

• It can provide more precise estimates of treatment effects by integrating direct and indirect evidence from multiple trials.

• It can provide a more comprehensive understanding of the relative effectiveness of different interventions.

• It can allow for the identification of the most effective intervention among several options, even if it has not been directly compared to all the others.

However, NMA also has some limitations, including:

• It requires the assumption of transitivity, which means that the characteristics of the trials and their populations are balanced across the different interventions.

• It requires a sufficient number of trials and interventions to construct a meaningful network.

• It requires careful consideration of the quality of the trials and the risk of bias, as well as the potential for heterogeneity and inconsistency.

In summary, network meta-analysis is a powerful statistical method that allows for the comparison of multiple interventions simultaneously, even when head-to-head comparisons are not available. It can provide more precise and comprehensive estimates of treatment effects and can help identify the most effective intervention among several options.

References:

1. Network meta-analysis: an introduction for clinicians - PubMed (nih.gov) 

2. Chapter 11: Undertaking network meta-analyses | Cochrane Training 

3. Network Meta-analysis | Cochrane Comparing Multiple Interventions 

4. Network Meta-Analysis Using R for Diabetes Data | IntechOpen