What is ML-NMR used for?

Multilevel Network Meta-Regression (ML-NMR) is used to synthesize evidence from a network of clinical trials where some studies provide Individual Patient Data (IPD) and others provide only Aggregate Data (AgD), accounting for population differences.

Does this skill help with R programming?

Yes, it provides specific code patterns and best practices for the multinma R package, including network setup, integration point specification, and model fitting.

How does it handle population adjustment?

The skill guides users through numerical integration techniques that sample from covariate distributions to ensure that individual-level models are correctly marginalized for comparison with aggregate-level studies.

Can it help with MCMC diagnostics?

Absolutely. It provides guidance on interpreting R-hat and Effective Sample Size (ESS) metrics, as well as strategies for addressing divergent transitions and convergence issues in Bayesian models.

ML-NMR Methodology Specialist

Name: ML-NMR Methodology Specialist
Author: choxos

bychoxos

0•

Data Science & ML

Provides expert methodological guidance and implementation patterns for Multilevel Network Meta-Regression (ML-NMR) in evidence synthesis.

The ML-NMR Methodology skill is designed for researchers and data scientists performing complex evidence synthesis, particularly when combining Individual Patient Data (IPD) with Aggregate Data (AgD). It provides deep technical knowledge on using the multinma R package, adhering to NICE DSU standards, and implementing population-adjustment techniques. Whether you are setting up numerical integration points, troubleshooting MCMC convergence in Stan, or interpreting marginal vs. conditional effects for Health Technology Assessment (HTA), this skill ensures your analysis follows rigorous statistical best practices.

Key Features

010 GitHub stars

02Consistency assessment via node-splitting in meta-regression

03NICE DSU-compliant methodological guidance for evidence synthesis

04MCMC convergence diagnostic troubleshooting (R-hat, ESS)

05Implementation patterns for the multinma R package

06Population-adjustment and numerical integration configuration

Use Cases

01Synthesizing clinical trial networks containing both IPD and AgD

02Reviewing and validating complex network meta-regression code

03Predicting treatment effects for specific target policy populations

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/itc-agents ml-nmr-methodology

For use in Claude.ai and ChatGPT

Download Skill