BiostatAgent

Comprehensive Biostatistics Agent Ecosystem — 30 specialized agents, 17 workflow commands, and 34 methodology skills for R-based statistical analysis

A unified Claude Code plugin marketplace consolidating four specialized biostatistics plugins:

Plugin	Focus	Agents	Commands	Skills
bayesian-modeling	Bayesian inference (Stan, PyMC, JAGS)	6	3	9
itc-modeling	Indirect treatment comparisons	7	2	6
r-tidy-modeling	Tidy R workflows & biostatistics	10	7	12
clinical-trial-simulation	Clinical trial simulation	7	5	7
Total		30	17	34

Quick Start

1. Add the Marketplace

/plugin marketplace add choxos/BiostatAgent

2. Install Plugins

Install all plugins or select specific ones:

# Install all
/plugin install bayesian-modeling itc-modeling r-tidy-modeling clinical-trial-simulation

# Or install individually
/plugin install bayesian-modeling
/plugin install itc-modeling
/plugin install r-tidy-modeling
/plugin install clinical-trial-simulation

3. Install R Dependencies

# Core dependencies (install as needed)
install.packages(c(
  # Bayesian modeling
  "cmdstanr", "rstan", "R2jags", "R2WinBUGS", "bayesplot", "loo",
  # ITC/NMA
  "meta", "netmeta", "gemtc", "multinma", "maicplus",
  # Tidy modeling
  "tidyverse", "tidymodels", "recipes", "parsnip", "workflows",
  # Clinical trials
  "simtrial", "Mediana", "gsDesign2", "survival"
))

---

Plugins Overview

1. Bayesian Modeling (bayesian-modeling)

Create, review, and validate Bayesian models across four languages:

• Stan 2.37 — Modern HMC/NUTS sampling with cmdstanr
• PyMC 5 — Python-native Bayesian modeling with ArviZ
• JAGS — Cross-platform Gibbs sampling with R2jags
• WinBUGS — Classic BUGS implementation with R2WinBUGS

Commands:

Command	Description
/create-model	Interactive model creation workflow
/review-model	Review existing models for correctness
/run-diagnostics	Test model execution with synthetic data

Agents: model-architect, stan-specialist, pymc-specialist, bugs-specialist, model-reviewer, test-runner

---

2. ITC Modeling (itc-modeling)

Expert agents for indirect treatment comparison following NICE DSU guidance:

• Pairwise Meta-Analysis — Fixed/random effects with meta, metafor, bayesmeta
• Network Meta-Analysis — Frequentist (netmeta) and Bayesian (gemtc)
• MAIC — Matching-adjusted indirect comparison with maicplus
• STC — Simulated treatment comparison
• ML-NMR — Multilevel network meta-regression with multinma

Commands:

Command	Description
/itc-analysis	Full ITC workflow from method selection to results
/itc-review	Review existing ITC code for methodological issues

Agents: itc-architect, pairwise-meta-analyst, nma-specialist, maic-specialist, stc-specialist, ml-nmr-specialist, itc-code-reviewer

---

3. R Tidy Modeling (r-tidy-modeling)

Comprehensive R data science following tidyverse and tidymodels best practices:

• Data Wrangling — dplyr, tidyr, data transformation
• Feature Engineering — recipes, preprocessing, transformations
• Model Building — parsnip, workflows, tidymodels
• Visualization — ggplot2, publication-ready figures
• Reporting — Quarto, R Markdown, reproducible reports
• Biostatistics — Clinical trials, survival analysis, epidemiology

Commands:

Command	Description
/r-analysis	End-to-end analysis workflow
/r-code-review	Review R code for best practices
/r-model-comparison	Compare multiple models
/r-clinical-analysis	Clinical trial analysis workflow
/r-project-setup	Set up reproducible R project
/r-doc-generate	Generate documentation
/r-tutorial-create	Create tutorials from code

Agents: r-data-architect, tidymodels-engineer, feature-engineer, biostatistician, data-wrangler, viz-specialist, reporting-engineer, r-code-reviewer, r-docs-architect, r-tutorial-engineer

---

4. Clinical Trial Simulation (clinical-trial-simulation)

Design and simulate clinical trials using simtrial and Mediana:

• simtrial — Time-to-event simulations, weighted logrank, MaxCombo
• Mediana — Clinical Scenario Evaluation, multiplicity, Word reports
• gsDesign2 — Group sequential designs, alpha spending

Commands:

Command	Description
/power-analysis	Calculate power across scenarios
/sample-size	Find minimum sample size for target power
/gs-design	Design group sequential trials
/multiplicity-optimization	Optimize multiple testing procedures
/cse-analysis	Full Clinical Scenario Evaluation

Agents: simulation-architect, tte-specialist, cse-specialist, multiplicity-expert, gs-design-specialist, power-optimizer, code-reviewer

---

Repository Structure

BiostatAgent/
├── .claude-plugin/
│   └── marketplace.json              # Unified plugin manifest
├── plugins/
│   ├── bayesian-modeling/
│   │   ├── agents/                   # 6 agents
│   │   ├── commands/                 # 3 commands
│   │   └── skills/                   # 9 skills
│   ├── itc-modeling/
│   │   ├── agents/                   # 7 agents
│   │   ├── commands/                 # 2 commands
│   │   └── skills/                   # 6 skills
│   ├── r-tidy-modeling/
│   │   ├── agents/                   # 10 agents
│   │   ├── commands/                 # 7 commands
│   │   └── skills/                   # 12 skills
│   └── clinical-trial-simulation/
│       ├── agents/                   # 7 agents
│       ├── commands/                 # 5 commands
│       └── skills/                   # 7 skills
├── README.md
└── LICENSE

---

Usage Examples

Bayesian Modeling

Create a hierarchical model for patient outcomes nested within hospitals.
Use Stan with cmdstanr.

ITC Analysis

I have IPD for trial A and AgD for trial B.
Help me run a MAIC to compare treatments.

Tidy R Analysis

Build a predictive model for patient readmission using tidymodels.
Include cross-validation and hyperparameter tuning.

Clinical Trial Simulation

Calculate power for a survival trial with HR=0.7, 300 events, alpha=0.025.
Use weighted logrank for non-proportional hazards.

---

All Components

Agents (30 total)

Plugin	Agents
bayesian-modeling	model-architect, stan-specialist, pymc-specialist, bugs-specialist, model-reviewer, test-runner
itc-modeling	itc-architect, pairwise-meta-analyst, nma-specialist, maic-specialist, stc-specialist, ml-nmr-specialist, itc-code-reviewer
r-tidy-modeling	r-data-architect, tidymodels-engineer, feature-engineer, biostatistician, data-wrangler, viz-specialist, reporting-engineer, r-code-reviewer, r-docs-architect, r-tutorial-engineer
clinical-trial-simulation	simulation-architect, tte-specialist, cse-specialist, multiplicity-expert, gs-design-specialist, power-optimizer, code-reviewer

Commands (17 total)

Plugin	Commands
bayesian-modeling	/create-model, /review-model, /run-diagnostics
itc-modeling	/itc-analysis, /itc-review
r-tidy-modeling	/r-analysis, /r-code-review, /r-model-comparison, /r-clinical-analysis, /r-project-setup, /r-doc-generate, /r-tutorial-create
clinical-trial-simulation	/power-analysis, /sample-size, /gs-design, /multiplicity-optimization, /cse-analysis

Skills (34 total)

Plugin	Skills
bayesian-modeling	stan-fundamentals, pymc-fundamentals, bugs-fundamentals, hierarchical-models, regression-models, time-series-models, survival-models, meta-analysis, model-diagnostics
itc-modeling	tidy-itc-workflow, pairwise-ma-methodology, nma-methodology, maic-methodology, stc-methodology, ml-nmr-methodology
r-tidy-modeling	tidymodels-workflow, recipes-patterns, resampling-strategies, model-tuning, model-evaluation, survival-analysis, clinical-trials, bayesian-modeling, epidemiology-methods, genomics-analysis, r-documentation-patterns, roxygen2-pkgdown
clinical-trial-simulation	simtrial-fundamentals, mediana-fundamentals, multiplicity-methods, time-to-event-methods, group-sequential-methods, power-optimization-patterns, clinical-trial-design-patterns

---

License

MIT License — see LICENSE for details.

Acknowledgments

• Stan Development Team for Stan
• PyMC Developers for PyMC
• Merck & Co. for simtrial
• Mediana Inc. for Mediana
• tidymodels team for tidymodels
• Anthropic for Claude Code