Which R packages does this skill support for NMA?

The skill supports leading R packages including netmeta for frequentist analysis, and gemtc and multinma for Bayesian approaches using JAGS or Stan.

Can I visualize the treatment network geometry?

Yes, it provides implementation patterns for netgraph and other visualization tools to show treatment nodes, comparison thickness, and evidence density.

How are treatment rankings calculated?

The skill includes code for calculating P-scores in frequentist models and SUCRA (Surface Under the Cumulative Ranking) or rank probabilities in Bayesian models.

Does it handle inconsistency between direct and indirect evidence?

Yes, it includes methods for both global inconsistency testing (design-by-treatment interaction) and local assessment via node-splitting.

Does this skill support individual patient data (IPD)?

Yes, it includes specific patterns for the multinma package which allows combining aggregate data (AgD) with individual patient data for population-adjusted NMA.

Network Meta-Analysis in R

Name: Network Meta-Analysis in R
Author: choxos

bychoxos

0•

데이터 과학 및 ML

Performs advanced multi-treatment comparisons using frequentist and Bayesian network meta-analysis methods in R.

This skill provides a comprehensive framework for conducting Network Meta-Analysis (NMA) in R, allowing researchers to synthesize evidence from multiple treatment comparisons simultaneously. It covers the full analytical pipeline—from data preparation (pairwise and arm-level) and network visualization to sophisticated frequentist and Bayesian modeling using industry-standard packages like netmeta, gemtc, and multinma. Designed for biostatisticians and clinical researchers, the skill includes specialized patterns for assessing network consistency, evaluating heterogeneity, generating treatment rankings via SUCRA or P-scores, and performing advanced meta-regression to ensure rigorous evidence synthesis.

주요 기능

01Comprehensive consistency assessment via node-splitting and design-by-treatment interaction models

02Advanced modeling for population-adjusted NMA and network meta-regression

030 GitHub stars

04Advanced network visualization including geometry graphs and net heat plots

05Automated treatment ranking tools including SUCRA, P-scores, and rankograms

06Support for frequentist (netmeta) and Bayesian (gemtc, multinma) NMA frameworks

사용 사례

01Ranking competing treatments in a therapeutic area to inform clinical guidelines

02Evaluating the consistency between direct and indirect evidence in complex clinical trial networks

03Comparing the relative efficacy of multiple medical interventions in the absence of head-to-head trials

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent network-meta-analysis

For use in Claude.ai and ChatGPT

주요 기능

01Comprehensive consistency assessment via node-splitting and design-by-treatment interaction models

02Advanced modeling for population-adjusted NMA and network meta-regression

030 GitHub stars

04Advanced network visualization including geometry graphs and net heat plots

05Automated treatment ranking tools including SUCRA, P-scores, and rankograms

06Support for frequentist (netmeta) and Bayesian (gemtc, multinma) NMA frameworks

사용 사례

01Ranking competing treatments in a therapeutic area to inform clinical guidelines

02Evaluating the consistency between direct and indirect evidence in complex clinical trial networks

03Comparing the relative efficacy of multiple medical interventions in the absence of head-to-head trials