nma-methodology

To assess transitivity and consistency in network meta-analysis, evaluate whether trials are similar in key effect modifiers, then compare direct and indirect estimates. This Skill provides actionable steps for both planning and reviewing these assessments.

Choosing between Bayesian and frequentist network meta-analysis depends on your inference goals and data complexity. This Skill provides practical modeling recommendations to help you select the appropriate approach for your specific research scenario.

Interpreting treatment rankings in network meta-analysis involves understanding probability outputs and rankograms. This Skill guides you through evaluating ranking robustness and applying correct interpretation aligned with PRISMA-NMA guidelines.

Planning a network meta-analysis using NICE DSU guidelines requires structured methodological coverage from transitivity checks to model selection. This Skill delivers deep guidance aligned with TSDs to ensure robust evidence synthesis.

Validating network meta-analysis code for fixed and random effects models involves checking model assumptions and consistency. This Skill supports data analysts by providing methodological knowledge for code review and result interpretation.

Yes, this network meta-analysis guidance covers both research planning and code review scenarios. It provides comprehensive methodological coverage for designing analyses, validating code, and interpreting results with actionable steps.