Is it compliant with reporting standards?

Yes, the skill follows NICE DSU Technical Support Documents and PRISMA-NMA guidelines to ensure all findings are reported transparently and rigorously.

Can it help with transitivity assessment?

Absolutely, it includes detailed workflows for checking population similarity, outcome definitions, and study designs to ensure the transitivity assumption is met.

How does it handle treatment rankings?

It explains how to calculate and interpret P-scores, SUCRA, and ranking probabilities while emphasizing the importance of uncertainty measures and clinical relevance.

Does this skill support both Frequentist and Bayesian NMA?

Yes, it provides guidance and code for both Frequentist frameworks using the netmeta package and Bayesian frameworks using gemtc.

Does it provide code snippets for R?

Yes, it offers ready-to-use R code for fitting models, generating network graphs, league tables, and performing node-splitting analysis.

Network Meta-Analysis (NMA) Methodology

Name: Network Meta-Analysis (NMA) Methodology
Author: choxos

bychoxos

0•

Data Science & ML

Provides comprehensive methodological guidance and R implementation patterns for conducting rigorous network meta-analyses.

This skill equips Claude with deep expertise in Network Meta-Analysis (NMA), adhering to industry-standard NICE DSU and PRISMA-NMA guidelines. It enables the AI to assist researchers in assessing transitivity and consistency, performing frequentist or Bayesian model selection, and generating accurate treatment rankings using R packages like netmeta and gemtc. Whether you are planning a study, reviewing code, or interpreting complex statistical results, this skill ensures methodological rigor and adherence to best practices in biostatistics and evidence synthesis.

Key Features

010 GitHub stars

02Automated checklist for PRISMA-NMA reporting compliance

03Transitivity and consistency assessment workflows

04Treatment ranking interpretation (P-scores, SUCRA, and Prob_best)

05Frequentist and Bayesian NMA model selection guidance

06R code generation for netmeta and gemtc packages

Use Cases

01Debugging or reviewing R scripts for NMA consistency tests and node-splitting

02Planning and designing the statistical framework for a new network meta-analysis

03Interpreting complex ranking probabilities and clinical significance in research outputs

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent nma-methodology

For use in Claude.ai and ChatGPT

Key Features

010 GitHub stars

02Automated checklist for PRISMA-NMA reporting compliance

03Transitivity and consistency assessment workflows

04Treatment ranking interpretation (P-scores, SUCRA, and Prob_best)

05Frequentist and Bayesian NMA model selection guidance

06R code generation for netmeta and gemtc packages

Use Cases

01Debugging or reviewing R scripts for NMA consistency tests and node-splitting

02Planning and designing the statistical framework for a new network meta-analysis

03Interpreting complex ranking probabilities and clinical significance in research outputs

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent nma-methodology

For use in Claude.ai and ChatGPT