Does this skill provide code for both Frequentist and Bayesian approaches?

Yes, it includes code snippets and guidance for Frequentist methods using the R package 'netmeta' and Bayesian methods using 'gemtc' and 'rjags'.

Can this skill help me assess the transitivity assumption?

Absolutely. It provides a detailed framework for assessing population similarity, outcome definitions, and study designs to ensure valid indirect comparisons.

What standards does this NMA skill follow?

It follows internationally recognized guidelines including NICE DSU (Decision Support Unit) Technical Support Documents and the PRISMA-NMA reporting checklist.

What should I do if my model shows inconsistency?

The skill provides a step-by-step troubleshooting guide, including checking data entry, investigating node-splitting results, and exploring heterogeneity via meta-regression.

How does the skill handle treatment rankings?

It offers guidance on calculating and interpreting P-scores for frequentist models and SUCRA (Surface Under Cumulative Ranking Curve) for Bayesian models, including uncertainty measures.

Network Meta-Analysis (NMA) Methodology

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

bychoxos

데이터 과학 및 ML

Provides comprehensive methodological guidance and code implementations for conducting rigorous network meta-analyses following international standards.

소개

This skill equips Claude with deep expertise in Network Meta-Analysis (NMA) methodology, enabling it to assist researchers and data scientists in planning, executing, and reviewing complex indirect treatment comparisons. It covers critical theoretical foundations like transitivity and consistency assessment, offers practical code snippets for R packages like netmeta and gemtc, and ensures adherence to reporting standards like NICE DSU and PRISMA-NMA. Use it to select between fixed and random effects models, interpret treatment rankings using SUCRA or P-scores, and troubleshoot statistical inconsistencies in multi-arm trial networks.

주요 기능

Frequentist and Bayesian NMA model selection guidance
Implementation snippets for R packages netmeta and gemtc
Interpretation logic for treatment rankings (SUCRA, P-scores)
0 GitHub stars
Transitivity and consistency assessment frameworks
PRISMA-NMA and NICE DSU reporting checklists

사용 사례

Troubleshooting and investigating statistical inconsistency in research data
Planning and designing a systematic review with network meta-analysis
Generating R code for treatment rankings and network forest plots

소개

주요 기능

Frequentist and Bayesian NMA model selection guidance
Implementation snippets for R packages netmeta and gemtc
Interpretation logic for treatment rankings (SUCRA, P-scores)
0 GitHub stars
Transitivity and consistency assessment frameworks
PRISMA-NMA and NICE DSU reporting checklists

사용 사례

Troubleshooting and investigating statistical inconsistency in research data
Planning and designing a systematic review with network meta-analysis
Generating R code for treatment rankings and network forest plots