ML-NMR Methodology Guide

소개

This skill empowers users to conduct sophisticated Multilevel Network Meta-Regression (ML-NMR) by bridging the gap between Individual Patient Data (IPD) and Aggregate Data (AgD). Following NICE DSU TSD 18 standards and the 'multinma' R package documentation, it provides detailed frameworks for population adjustment, numerical integration, and prior specification. It is particularly useful for clinical researchers and health economists who need to synthesize evidence across heterogeneous trial populations, ensure model consistency, and predict treatment effects for specific target populations in health technology assessments.

주요 기능

• Detailed protocols for numerical integration and population adjustment
• Advanced consistency assessment methods including node-splitting
• Comprehensive guidance for IPD and AgD integration using multinma patterns
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• Prior specification templates for treatment effects and heterogeneity
• Best practices for MCMC convergence diagnostics and reporting

사용 사례

• Generating population-adjusted predictions for policy-relevant target groups
• Conducting health technology assessments with mixed IPD and AgD evidence networks
• Adjusting for treatment effect modifiers across different trial populations