Network meta-analysis framework

Why does Ki use network meta-analysis?

Meta-analytic methods provide comparative effectiveness research for interventions of the same condition. By utilizing the meta-analysis methods, the synthesis of numerous studies can provide precise estimates and inform clinical practice for the promotion of healthy growth. Network meta-analysis is a subset of meta-analysis that leverages data from direct evidence to estimate indirect comparisons between two comparators that are not directly compared.

WHAT ARE META-ANALYSIS AND NETWORK META-ANALYSIS?

Traditional meta-analysis is a pairwise approach to consolidate effect estimates between a single intervention and control. This approach calculates a weighted average effect across multiple studies.[1]

Network meta-analysis allows comparisons of multiple interventions for the same health condition by using indirect and direct comparisons.[1, 2]

Network diagrams include nodes (specific interventions) and lines that connect the nodes (which represent a randomized clinical trial [RCT]) to visualize the direct estimates (Figure 1).

FIGURE 1. Indirect and direct evidence[2]

Direct evidence, also referred to as head-to-head evidence, is the estimate reported from an RCT between two nodes. (2) Indirect comparisons are conducted by identifying a common comparator between two different interventions and calculating an indirect estimate of the effect (Figure 2).

An example of an indirect estimate is between treatment B and C (no line connecting the two nodes). In this example, treatment B has an estimated effect that is significantly lower effect than treatment C, although they were never directly compared.

FIGURE 2. Estimated effect measures and network plot [2]

Statistical methods

There are two primary approaches to statistical analysis of NMA: Frequentist and Bayesian.[2]

Local or global approaches test the model for inconsistency.

The local approach determines the presence of inconsistency in a specific head-to-head comparison in the network (i.e. if two trials of the same intervention have different outcomes, then the test for local approach consistency is violated). The global approach evaluates inconsistency across the entire network (i.e. when comparing indirect trials and there are different outcomes, then the test for global approach consistency is violated).

NMA requires at least 3 comparators. The availability of multiple trials for each comparator is advantageous.[1]

Example: A valid indirect estimate can be calculated between intervention A and intervention B, if a direct estimate of intervention A versus placebo A and a direct estimate of intervention B versus placebo A were obtained among participants with similar baseline comorbidities, treatment doses, and of similar study quality.

FIGURE 3. Network diagram of micronutrient supplementation and deworming for children from Rally 4B.[3] The numbers are the number of direct comparisons from RCTs for each connecting line.

Advantages of network meta-analysis

NMA improves the precision of estimates (reduces the confidence interval) by combination of multiple study results and calculating a relative treatment effect for all treatment interventions.

Graphical representation of the network helps describe the evidence. Nodes or edges can be proportional to the amount of data available from direct comparisons (Figure 3).

 

Disadvantages of network meta-analysis

Resource Links

 

References
  1. Hutton B. Cochrane Canada webinar on NMA with Brian Hutton. 2014; https://www. youtube.com/watch?time_ continue=1160&v= IVY30A3UWbI. Accessed November 10, 2017.
  2. Tonin FS, Rotta I, Mendes AM, Pontarolo R. Network meta-analysis: a technique to gather evidence from direct and indirect comparisons. Pharmacy Practice. 2017;15(1):943.
  3. Bill & Melinda Gates Foundation. (2017). Rally 4B. Descriptive Epidemiology of Wasting: Characteristics Associated with Incident Wasting and Initial Recovery. Retrieved from http:// mailchi.mp/838348a33624/ hbgdki-leadership-update-rally-2a-explores-question-about-fetal-grown-maternal-covariates-and-birth-outcomes- 177773?e=cc8cfb09f2#LU10kiR4B
  4. Salanti G. 10-minute introduction to NMA with Georgia Salanti. 2016; https://www.youtube.com/ watch?v=xaCEiB9MI6c. Accessed November 10, 2017.