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

데이터 과학 및 ML

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

소개

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.

주요 기능

0 GitHub stars
Consistency assessment via node-splitting in meta-regression
NICE DSU-compliant methodological guidance for evidence synthesis
MCMC convergence diagnostic troubleshooting (R-hat, ESS)
Implementation patterns for the multinma R package
Population-adjustment and numerical integration configuration

사용 사례

Synthesizing clinical trial networks containing both IPD and AgD
Reviewing and validating complex network meta-regression code
Predicting treatment effects for specific target policy populations

소개

주요 기능

0 GitHub stars
Consistency assessment via node-splitting in meta-regression
NICE DSU-compliant methodological guidance for evidence synthesis
MCMC convergence diagnostic troubleshooting (R-hat, ESS)
Implementation patterns for the multinma R package
Population-adjustment and numerical integration configuration

사용 사례

Synthesizing clinical trial networks containing both IPD and AgD
Reviewing and validating complex network meta-regression code
Predicting treatment effects for specific target policy populations