Jul 29

The key assumptions of network meta-analysis

Our first post in this blog, provided a brief introduction to network meta-analysis.

Read the first blog here

Like all statistical analyses, network meta-analysis relies on a specific set of assumptions. In this second post, we’ll look at the key assumptions of network meta-analysis. In order to draw valid conclusions about the relative efficacy and safety of treatments in a network meta-analysis and facilitate good decision making, it is important to understand and evaluate these assumptions.

Network meta-analysis is simply an extension to ordinary pairwise meta-analysis, and hence the assumptions of network meta-analysis are very similar to the assumptions of meta-analysis. The validity of both meta-analyses and network meta-analyses is dependent upon:

  • the adequacy of the evidence base and
  • the similarity of the trials.


Adequacy of the evidence base

The adequacy of the evidence base depends on both internal and external factors. Internally, when developing a network meta-analysis, you should use a systematic review to identify the relevant studies. Using a systematic approach ensures that there is no bias in the selection of the studies. Guidelines for health technology assessment submissions and network meta-analysis publications (e.g. the NICE Guide to the Methods of Technology Appraisal (1) and the PRISMA extension statement for network meta-analysis (2)) all require that network meta-analyses are based on a systematic review.

Externally, the adequacy of the evidence base depends upon the quality of the relevant studies and whether they have been fully published. Meta-analysis and network meta-analysis are only as good as the studies they are based on. For individual studies, important considerations include how patients were randomized to the treatments, whether both patients and outcome assessors were blind to the treatment and how missing data were handled. The Cochrane risk of bias tool (3) is commonly used to assess the quality of studies. If some studies have a high risk of bias, then sensitivity analysis with and without these studies is recommended.

In meta-analysis and network meta-analysis, publication bias is also a key concern. Publication bias arises when positive results are more likely to be published than negative results. Publication bias can occur both at the study level, when entire studies are left unpublished, or at the outcome level, when results are only partially published. Methods for assessing publication bias in meta-analysis are well established (e.g. funnel plots (4)), and where there is sufficient data, these can be applied to individual pairwise comparisons within a network. Alternatively, methods for assessing publication bias across entire networks, have also been proposed (5, 6).


Similarity of the trials

In both meta-analysis and network meta-analysis it is important to consider the similarity of the trials. The key assumption is that the trials shouldn’t differ in any characteristics that may impact the treatment effect. Here, it is important to differentiate between treatment responses and treatment effects. We define a treatment response, as how patients react to an individual treatment, and a treatment effect as the difference in response between two treatments. To illustrate this, consider a trial that compares two treatments for tiredness – tea and coffee.

Figure 1: Treatment responses and treatment effects.

HTA Blog 2_Fig 1

Figure 1 illustrates three scenarios for the relationship between the treatments (tea and coffee), the outcome (reduction in tiredness) and age. In Scenario 1, age doesn’t affect treatment response for either tea or coffee, hence there is also no impact on the treatment effect. In Scenario 2, age reduces the response to both tea and coffee, but note that the relationship is the same for each treatment, and hence the treatment effect is constant. In Scenario 3, age again reduces the response to both tea coffee, but note that in this case, age has a greater impact on coffee. In this case, the treatment effect depends on age. For Scenarios 1 and 2, we would say that age is not a treatment effect modifier. In each of these cases, it would be appropriate to conduct a meta-analysis that combines studies of young patients with studies of old patients. For Scenario 3, age reduces the response to coffee, but not to tea. It would not be appropriate to conduct a meta-analysis that combines studies of young patients with studies of old patients (a better approach would be to use a meta-regression, but that’s a topic for another post!).

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Both study design characteristics and patient characteristics are important. Following on with our example, key study design characteristics may include the location and setting of the trial (Italy or America, at home or at work), the formulation of the treatment (instant coffee or an espresso), and the time points when key outcomes were measured (15 minutes post treatment or 2 hours post treatment).  Key patient characteristics might include whether concomitant medications are allowed (e.g. Red Bull) and the severity of the condition (common Monday morning tiredness or new parent tiredness).

In network meta-analysis, we want all of the trials in the network to be similar with respect to any characteristics that are potential treatment effect modifiers. This assumption can be broken down by looking separately at each of homogeneity, transitivity (similarity) and consistency. In order to illustrate these concepts we expand our tiredness example to include a control, decaffeinated tea or coffee. Figure 2 provides a network diagram – it shows that, as well as coffee versus tea trials, there are also trials of coffee versus decaf (coffee), and tea versus decaf (tea).

Figure 2: Network diagram

Health Technology Assessment Network Meta Analysis

Homogeneity refers to the equivalence of trials within each pairwise comparison in the network. Hence we can examine homogeneity separately for the coffee versus tea trials, the coffee versus decaf trials and the tea versus decaf trials. For each comparison, we can assess the degree of heterogeneity qualitatively by reviewing the trial characteristics, but also quantitatively by comparing the treatment effects estimated by each trial. The I-squared statistic (7) is commonly used to quantify the degree of heterogeneity within a pairwise comparison.

Transitivity (or similarity, as it is referred to in some of the literature) concerns the validity of making indirect comparisons. In our example, consider whether it is appropriate to estimate the effect of coffee versus tea via the trials that include decaf as a control. Unlike homogeneity, transitivity cannot be evaluated quantitatively. Transitivity must be evaluated by carefully reviewing the characteristics of the trials. In our example, an important consideration is whether decaf coffee can be considered equivalent to decaf tea. If decaf coffee and decaf tea are likely to lead to different responses, then an indirect comparison which assumes they are equivalent, would not be appropriate.

Consistency refers to the equivalence of direct and indirect evidence. In our example, we can estimate the effect of coffee versus tea directly from the coffee versus tea trials, or indirectly via the decaf trials. Like heterogeneity, inconsistency can be assessed both qualitatively (by reviewing the trial characteristics) and quantitatively. In our example, to assess inconsistency quantitatively, we just compare the direct estimate with the indirect estimate. For complex networks, more sophisticated methods such as node-splitting (8) can be used to assess inconsistency.

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We hope you’ve found this brief introduction to homogeneity, transitivity and consistency valuable. If you’d like to learn more about these concepts then we would recommend Salanti’s very thorough discussion (in reference (9)).

This brings us to the end of our post on the key assumptions of network meta-analysis. In order to conduct appropriate analyses and facilitate good decision making, a network meta-analysis must be based on an adequate evidence base and the trials must be similar with respect to treatment effect modifiers.

Further information

If there are any particular topics you’d like to see covered in the future then please let us know. If you are looking for an in depth intro to network meta-analysis YHEC and Quantics run joint courses – currently available in-house, on request. Some slides from one session of this course are available here.

Get in touch


(1) National Institute for Health and Care Excellence. Guide to the methods of technology appraisal. 2013.

(2) Hutton B et al. The PRISMA Extension Statement for Reporting of Systematic Reviews Incorporating Network Meta-analyses of Health Care Interventions: Checklist and Explanations. Annals of Internal Medicine. 2015; 162: 777-784.

(3) Higgins JP et al. The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ. 2011; 343: d5928.

(4) Egger M et al. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997; 315: 629-634.

(5) Mavridis D et al. A selection model for accounting for publication bias in a full network meta‐analysis. Statistics in Medicine. 2014; 33(30): 5399-412.

(6) Trinquart L et al. A test for reporting bias in trial networks: simulation and case studies. BMC Medical Research Methodology. 2014; 14: 112.

(7) Higgins J et al. Measuring inconsistency in meta-analyses. BMJ. 2003; 327: 557-559.

(8) Dias S et al. Checking consistency in mixed treatment comparison meta-analysis. Statistics in Medicine. 2010; 29(7-8): 932–944.

(9) Salanti G. Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Res Synth Methods. 2012; 3: 80–97.


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About The Author

Kelly joined Quantics from a post as a statistician with Emphron in Australia in 2007. She has a degree in Mathematics from the University of Queensland, and a Masters in Statistics from the University of Sheffield. Kelly initially developed Quantics’ interest in bioassay, but following her MSc dissertation on network meta-analysis she helped build the Health Technology Assessment team at Quantics. Kelly left Quantics in 2017 to return to academia.

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