Network Meta-Analysis Services
When more than two treatments or interventions compete and no single trial has compared them all, network meta-analysis brings the whole evidence base together—ranking options and estimating comparisons that were never tested head-to-head. We design and deliver network meta-analyses to current methodological standards.
Network meta-analysis (NMA) is a meta-analysis method that compares three or more treatments simultaneously by combining direct evidence (from head-to-head trials) with indirect evidence (through common comparators). It estimates all pairwise comparisons—even those never tested directly—and ranks the options, provided the network is connected and consistent.
What network meta-analysis does
A conventional (pairwise) meta-analysis answers one question at a time: does treatment A beat treatment B? But most real decisions involve a field of competing options—several drugs, several interventions, several policies—and the trials that exist rarely compare all of them against each other. Some pairs have been tested head-to-head; many have not. Network meta-analysis solves this by analysing the entire network of comparisons at once.
It works by combining two kinds of evidence. Direct evidence comes from trials that compared two treatments head-to-head. Indirect evidence is inferred through a common comparator: if A was compared with C, and B was compared with C, then A and B can be compared indirectly through C—even though no trial ever pitted A against B. NMA pools direct and indirect evidence coherently across the whole network, producing an estimate for every pairwise comparison and, often, a ranking of all the treatments.
When to use it
Network meta-analysis is the right tool when your review question involves three or more interventions and you need to compare them against one another—particularly when the direct, head-to-head evidence is incomplete. It is widely used in health technology assessment and clinical guideline development, and increasingly in management, education, and policy research wherever multiple interventions target the same outcome.
If your question is simply “does A work better than B,” a standard pairwise meta-analysis is sufficient; NMA earns its extra complexity only when several options must be compared and ranked together.
Pairwise vs network meta-analysis
| Pairwise meta-analysis | Network meta-analysis | |
|---|---|---|
| Treatments compared | Two | Three or more, simultaneously |
| Evidence used | Direct only | Direct + indirect combined |
| Answers | Does A beat B? | How do all options compare and rank? |
| Key requirement | Comparable studies | Connected network + transitivity/consistency |
| Use when | One comparison matters | Many options must be ranked |
The assumptions that make it valid
NMA is powerful, but its credibility rests on assumptions that must be checked rather than assumed. The central one is transitivity: the studies making up the different comparisons must be similar enough in their participants, settings, and design that indirect comparisons are meaningful—in effect, that the common comparator behaves the same way across the network. Its statistical counterpart is consistency: where direct and indirect evidence exist for the same comparison, they should broadly agree. Where they diverge (inconsistency), the network’s results cannot be taken at face value until the source is understood.
A network also has to be connected—every treatment must link to the others through some chain of comparisons—or the disconnected part cannot be included. And, as in any synthesis, heterogeneity across studies and publication bias remain live concerns. A rigorous NMA reports the network geometry, tests transitivity and consistency explicitly, and is honest about where the evidence is thin.
Indirect evidence is only as good as the transitivity assumption. If the trials behind different comparisons differ systematically, indirect comparisons can mislead. Checking transitivity and consistency—not just running the model—is what separates a trustworthy network meta-analysis from a fragile one.
Ranking treatments—with caution
One of NMA’s most attractive outputs is a ranking of all the treatments, often summarised with statistics such as SUCRA. Rankings are useful but invite over-interpretation: a treatment can rank first while the evidence for it is sparse or uncertain, and small differences in rank are often not meaningful. We present rankings alongside the effect estimates and their uncertainty—never as a bare league table.
How we deliver a network meta-analysis
Network meta-analysis sits within our wider meta-analysis and evidence-synthesis service, run as part of a full systematic-review workflow—so the network is built on a sound review, not bolted onto a shaky one.
We begin with a registered protocol, a comprehensive and documented search, careful data extraction, and risk-of-bias assessment before any modelling. We then construct and visualise the network, choose an appropriate framework (frequentist or Bayesian) for the data and question, test transitivity and consistency, and fit the model.
Reporting follows the PRISMA extension for network meta-analyses, with methodological guidance drawn from established sources such as Cochrane.
You receive the network plot, a league table of all pairwise comparisons, ranking statistics, and a full assessment of heterogeneity and bias—all reported to PRISMA-NMA standards and delivered with reproducible code. The result is analysis you can defend in peer review, not a black-box output.
Network meta-analysis across Management & Allied Studies
Network meta-analysis originated in health technology assessment and remains most established there, but it applies wherever several interventions, policies, or strategies compete to influence the same outcome and a connected network of comparisons exists. We confirm that requirement before recommending it.
Management & Organisational Studies
Comparing leadership, HR, and change-management interventions on performance, engagement, or turnover—where trials rarely test every approach head-to-head.
Economics & Public Policy
Ranking competing policy instruments or programme designs on economic and social outcomes by pooling direct and indirect evidence across evaluations.
Marketing & Consumer Research
Comparing promotional, pricing, or communication strategies on conversion, loyalty, and brand outcomes across many separate studies.
Finance & Accounting
Applicable where several interventions or regimes have been compared across studies with shared comparators—less common here, so we confirm the network is connected before recommending NMA.
Education & Learning Sciences
Ranking teaching methods, edtech tools, or interventions on learning outcomes when head-to-head comparisons are scarce.
Operations & Information Systems
Comparing process, technology-adoption, or system-design interventions on efficiency, quality, and adoption outcomes.
Network meta-analysis: common questions
Comparing several treatments or interventions?
If your review question spans multiple options and the head-to-head evidence is incomplete, a network meta-analysis may be exactly what it needs. We scope, design, and deliver it to PRISMA-NMA standards.