nma-methodology

Guide network meta-analysis planning with transitivity and consistency assessments.

Updated Dec 8, 2025

Search Tags:#consistency #model-selection #bayesian #transitivity #network-meta-analysis #nma #frequentist

Ready to run

npx skills add https://github.com/choxos/ITC-agents --skill nma-methodology

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: nma-methodology
Source: https://github.com/choxos/ITC-agents/tree/main/plugins/itc-modelling/skills/nma-methodology
Command: npx skills add https://github.com/choxos/ITC-agents --skill nma-methodology

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Transitivity, consistency, and model selection in network meta-analyses can be complex and error-prone, and researchers often lack a single source of structured guidance for planning and reviewing these analyses.

Core Features & Use Cases

Provides deep methodological guidance for planning, conducting, and reviewing network meta-analyses, aligned with NICE DSU TSDs and PRISMA-NMA.
Covers transitivity checks, consistency assessments, treatment rankings interpretation, and model selection (fixed vs random, Bayesian vs frequentist).
Supports researchers and data analysts in designing analyses, validating code, and interpreting results with clear, actionable steps.

Quick Start

Review the network meta-analysis methodology guide to begin planning and evaluating your NMA with transitivity and consistency checks.

Frequently Asked Questions about nma-methodology

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I assess transitivity and consistency in network meta-analysis?▼

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.

Bayesian vs frequentist network meta-analysis: which model should I choose?▼

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.

How do I interpret treatment rankings in network meta-analysis?▼

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.

What is the best way to plan a network meta-analysis using NICE DSU 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.

How do I validate network meta-analysis code for fixed and random effects models?▼

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.

Does this network meta-analysis guidance cover both research planning and code review?▼

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.