# Network Meta Analysis

> Network meta-analysis in R, including network setup, consistency, treatment rankings, and league tables.

- **Type:** Skill
- **Install:** `agentstack add skill-choxos-biostatagent-network-meta-analysis`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [choxos](https://agentstack.voostack.com/s/choxos)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [choxos](https://github.com/choxos)
- **Source:** https://github.com/choxos/BiostatAgent/tree/main/plugins/r-tidy-modeling/skills/network-meta-analysis

## Install

```sh
agentstack add skill-choxos-biostatagent-network-meta-analysis
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Network Meta-Analysis in R

## Overview

Network meta-analysis (NMA) methods for comparing multiple treatments simultaneously using direct and indirect evidence. Covers network structure assessment, frequentist and Bayesian NMA approaches, consistency evaluation, treatment rankings, and visualization techniques.

## Network Structure and Data Preparation

### Pairwise Data Format

```r
library(netmeta)

# Standard pairwise format for contrast-based NMA
pairwise_data = 2.0)
# net_reg 
    mutate(
      Fixed = paste0(round(Effect_Fixed, 2), " (",
                    round(CI_Lower_Fixed, 2), ", ",
                    round(CI_Upper_Fixed, 2), ")"),
      Random = paste0(round(Effect_Random, 2), " (",
                     round(CI_Lower_Random, 2), ", ",
                     round(CI_Upper_Random, 2), ")")
    ) |>
    select(Comparison, Fixed, Random)

  return(summary_df)
}
```

## Key Packages Summary

| Package | Purpose |
|---------|---------|
| netmeta | Frequentist NMA (contrast-based) |
| gemtc | Bayesian NMA with JAGS |
| multinma | Bayesian NMA with Stan |
| bnma | Bayesian NMA |
| pcnetmeta | Patient-centered NMA |
| NMAoutlier | Outlier detection in NMA |
| nmathresh | Decision thresholds for NMA |

## Best Practices

1. **Network geometry**: Check connectivity before analysis
2. **Transitivity**: Assess similarity of study populations across comparisons
3. **Consistency**: Always assess local and global inconsistency
4. **Heterogeneity**: Report tau, I², and consider prediction intervals
5. **Ranking**: Present uncertainty (CrI for ranks, rankograms)
6. **Sensitivity**: Conduct analyses excluding high RoB studies
7. **Reporting**: Follow PRISMA-NMA extension guidelines
8. **Model selection**: Compare fixed vs random effects, check model fit

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [choxos](https://github.com/choxos)
- **Source:** [choxos/BiostatAgent](https://github.com/choxos/BiostatAgent)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-choxos-biostatagent-network-meta-analysis
- Seller: https://agentstack.voostack.com/s/choxos
- Browse the marketplace: https://agentstack.voostack.com/browse

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