1. Conceptual Foundations of Network Meta-Analysis (NMA)
Network meta-analysis extends pairwise meta-analysis by simultaneously comparing multiple interventions within a single analytical framework, integrating both direct and indirect evidence. The validity of NMA rests on the assumption of transitivity, which requires that studies comparing different treatment pairs are sufficiently similar in terms of effect modifiers. Violations of this assumption can lead to biased indirect comparisons and misleading treatment rankings.
2. Assessing and Interpreting Inconsistency in Networks
Inconsistency arises when direct and indirect evidence for the same comparison disagree beyond what would be expected by chance. Common approaches to detect inconsistency include the node-splitting method and the design-by-treatment interaction model. These methods help identify specific loops or comparisons contributing to inconsistency, but interpretation requires caution, as statistical inconsistency may also reflect clinical or methodological heterogeneity.
3. Frequentist vs Bayesian Frameworks in NMA
Frequentist NMA typically relies on multivariate meta-regression models and provides point estimates with confidence intervals, often implemented in statistical software such as STATA or R. Bayesian NMA, on the other hand, incorporates prior distributions and generates posterior estimates with credible intervals, allowing probabilistic interpretation of treatment effects. The choice between frameworks influences not only estimation but also how uncertainty and prior knowledge are formally integrated into the analysis.
4. Treatment Ranking and SUCRA Limitations
Surface Under the Cumulative Ranking curve (SUCRA) values are commonly used to rank treatments in NMA; however, high SUCRA scores do not necessarily imply clinically meaningful superiority. Rankings can be unstable in sparse networks or when effect sizes are similar across interventions. Therefore, SUCRA should be interpreted alongside absolute effect estimates, confidence or credible intervals, and clinical relevance rather than as a standalone decision metric.
5. Practical Implementation Challenges in Statistical Software
Implementing NMA in software such as SPSS is limited due to the absence of native network meta-analysis procedures, often requiring data restructuring and external macros or reliance on R-based packages. Even in advanced software, challenges include managing multi-arm trials, selecting appropriate variance structures, and ensuring reproducibility. Transparent reporting following PRISMA-NMA guidelines is essential to allow critical appraisal and replication of complex analytical decisions.
