Can this skill help with R implementation?

Yes, it includes quick-reference code snippets for the maicplus R package to help you center IPD, estimate weights, and run anchored MAIC analyses.

How do I handle a low Effective Sample Size (ESS)?

The skill provides a rubric for ESS interpretation, generally considering values < 30% as poor or unreliable, and suggests strategies such as removing balanced covariates to improve precision.

What is MAIC used for?

Matching-Adjusted Indirect Comparison (MAIC) is a statistical technique used to compare treatment effects across different clinical trials when individual patient data is available for one trial but only aggregate data is available for another.

Does this skill follow specific regulatory guidelines?

Yes, the guidance provided is aligned with the NICE Decision Support Unit (DSU) Technical Support Document 18 (TSD 18) for population-adjusted indirect comparisons.

MAIC Methodology Guide

Name: MAIC Methodology Guide
Author: choxos

bychoxos

数据科学与机器学习

Provides expert methodological guidance and best practices for conducting Matching-Adjusted Indirect Comparisons (MAIC) in health economics and outcomes research.

关于

This skill equips Claude with specialized expertise in Matching-Adjusted Indirect Comparison (MAIC) methodology, adhering to the NICE DSU TSD 18 guidelines. It assists researchers and statisticians in navigating complex decisions such as selecting effect modifiers, interpreting Effective Sample Size (ESS), and evaluating weight diagnostics. Whether you are conducting a new analysis, choosing between anchored and unanchored approaches, or reviewing existing results for bias, this skill ensures rigorous adherence to statistical assumptions and industry standards for indirect treatment comparisons.

主要功能

Decision framework for anchored vs. unanchored MAIC
Strategies for covariate selection and distribution balancing
0 GitHub stars
NICE DSU TSD 18 compliant methodological guidance
Implementation-ready R code snippets for the maicplus package
Detailed ESS interpretation and weight diagnostic checks

使用场景

Designing statistical analysis plans for indirect treatment comparisons
Reviewing MAIC results for potential bias and reliability of weight distributions
Generating and debugging R code for population-adjusted modeling

关于

主要功能

Decision framework for anchored vs. unanchored MAIC
Strategies for covariate selection and distribution balancing
0 GitHub stars
NICE DSU TSD 18 compliant methodological guidance
Implementation-ready R code snippets for the maicplus package
Detailed ESS interpretation and weight diagnostic checks

使用场景

Designing statistical analysis plans for indirect treatment comparisons
Reviewing MAIC results for potential bias and reliability of weight distributions
Generating and debugging R code for population-adjusted modeling