Network Meta-Analysis Manuscripts

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1. 01Enquiry and requirement assessment
2. 02Technical scoping and proposal
3. 03Execution
4. 04Quality review and delivery, with an agreed round of revisions

Read the full project workflow.

In depth: how the work is done

1. Describing the network geometry

   A network plot shows the interventions as nodes and the direct comparisons as edges, with node size or edge thickness indicating the amount of evidence. It reveals sparsely connected parts of the network, where estimates rely on few studies. We help authors prepare clear network plots and describe the geometry in the text.

2. Transitivity

   Transitivity requires that studies are similar in characteristics that influence the effect, such as material type, test conditions or datasets. The manuscript should list effect modifiers considered, compare their distributions across comparisons and discuss plausibility. Transitivity is an assumption that data can support but not prove.

3. Consistency and inconsistency

   Statistical tests, global and local, check agreement between direct and indirect evidence. Where inconsistency is found, authors investigate sources and consider changing the model or excluding comparisons. Reporting should show both the results and the investigation.

4. Ranking with uncertainty

   Rankings are popular but easily over-interpreted. Surface under the cumulative ranking curve and rank probabilities should be shown with confidence intervals for effect estimates, and rankings should not be presented without them. Small differences and wide intervals mean that rankings are unreliable.

5. Model specification and software

   The paper states whether a frequentist or Bayesian approach was used, the priors if Bayesian, the heterogeneity assumptions and the software. Convergence diagnostics are reported for Bayesian models. Sufficient detail allows replication.

6. Certainty of the network estimates

   Frameworks for rating certainty in network estimates consider risk of bias, indirectness, imprecision, inconsistency and publication bias. Applying one, even in simplified form, gives readers an idea of how far to trust each comparison.

7. Reporting guidelines

   The PRISMA extension for network meta-analyses lists items to report. We help authors complete the checklist and address missing items in the manuscript.

8. Presenting league tables and comparative results

   Results of a network meta-analysis are often shown in league tables giving every pairwise comparison with its interval. These tables can be dense, so highlighting statistically and practically meaningful differences, and using consistent direction of effect, helps readers. We help authors design tables and figures that communicate the full network without implying false precision, and we check that text and tables agree.

9. Sensitivity to network structure

   Removing a single influential study, excluding studies at high risk of bias or splitting nodes for different doses can change conclusions. Reporting such analyses shows whether the results are stable. If rankings change substantially, the manuscript should say so and moderate its conclusions. We help authors select and present sensitivity analyses concisely.

10. Applicability to engineering decisions

    Even a technically sound network meta-analysis gives relative effects under the conditions of the included studies. Practitioners need to know how far these conditions resemble theirs and what costs, risks and constraints apply. The discussion should address these considerations and avoid recommending a single best option universally. We help authors write balanced guidance that reflects both the statistical findings and the practical context.

Common pitfalls this service helps avoid

• Ignoring transitivity. Indirect comparisons can be invalid if studies differ systematically.
• Presenting ranks without intervals. Rankings can be unstable.
• Sparse networks. Few direct comparisons produce fragile estimates.
• Unreported model details. Prevents replication and assessment.

What good reporting looks like

Support for a network meta-analysis manuscript is documented with a network-geometry description, a transitivity and consistency reporting review, a model specification check and a PRISMA-NMA checklist record. The statistical work is done by the authors or analysts. We do not invent studies or results and make no acceptance promise.

An illustrative scenario

Hypothetical example written for explanation. It is not a client case, a result or a testimonial.

Suppose researchers compare five coating treatments across 22 studies, most comparing treatments with an untreated control. Indirect comparisons suggest one treatment is best, but test conditions differ across the studies that compared it. The authors report transitivity concerns, show wide intervals for indirect comparisons and present the ranking as tentative, recommending head-to-head tests under standard conditions.

Questions to settle before you begin

• Is my network well connected?
• What effect modifiers could violate transitivity?
• Do direct and indirect estimates agree?
• How uncertain are my rankings?
• Have I followed the network meta-analysis reporting guideline?

Key terms

Network meta-analysis

A method combining direct and indirect comparisons.

Transitivity

The similarity needed to make indirect comparisons valid.

Consistency

Agreement between direct and indirect evidence.

SUCRA

A summary measure of ranking.

Effect modifier

A factor that changes the size of an effect.

Working together on Review Article Writing projects

Review article engagements begin by choosing the review type that fits the question, because systematic, scoping, narrative and bibliometric reviews have different methods and reporting expectations. We ask for the topic, the target journal and any searches or papers you already have. The method is documented so that the review can be audited, and every source cited is checked against the original. We do not invent references or studies, and we state limitations such as database coverage. Acceptance depends on editors and reviewers and cannot be promised.

Few projects need a single service in isolation. These are the services most often combined with this one.

• Meta-Analysis Manuscripts

  Manuscript preparation for meta-analyses in which effect estimates from comparable studies have been pooled, reporting methods, heterogeneity and sensitivity analyses clearly.

• Engineering Meta-Analysis

  Quantitative synthesis of results across comparable engineering studies, where effect measures can be pooled and heterogeneity explored. Engineering data often differ in units, test conditions and reporting, so effect-sise selection and harmonisation get explicit attention.

• Systematic Review Manuscripts

  Manuscript preparation for systematic reviews that have been conducted with a defined protocol: reporting the search, selection, appraisal and synthesis in line with PRISMA 2020 reporting principles.