What is MAIC in biostatistics?

Matching-Adjusted Indirect Comparison (MAIC) is a statistical method used to compare treatment effects across different trials when individual patient data (IPD) is available for only one of the trials being compared.

Is this skill compliant with regulatory standards?

Yes, the methodology provided follows the NICE DSU TSD 18 guidelines, which are the standard for population-adjusted indirect comparisons in health technology assessments.

When should I use anchored MAIC over unanchored?

Anchored MAIC is strongly preferred whenever a common comparator arm exists in both trials. It is more robust because prognostic factors cancel out, requiring you only to adjust for effect modifiers.

What does a low Effective Sample Size (ESS) indicate?

A low ESS (typically below 30%) suggests that the populations in the two trials are very different or that too many covariates were included, making the weighted results potentially unreliable.

MAIC Biostatistics Methodology

Name: MAIC Biostatistics Methodology
Author: choxos

bychoxos

데이터 과학 및 ML

Provides expert guidance and best practices for conducting Matching-Adjusted Indirect Comparisons (MAIC) in biostatistical research.

소개

The maic-methodology skill equips Claude with deep expertise in performing and reviewing Matching-Adjusted Indirect Comparisons (MAIC), a critical technique in Health Technology Assessment (HTA). It provides structured guidance on the fundamental assumptions of population-adjusted indirect comparisons, criteria for covariate selection, and critical diagnostics like Effective Sample Size (ESS) and weight distribution. By following NICE DSU TSD 18 standards, it helps researchers navigate complex decisions between anchored and unanchored comparisons, ensuring rigorous and reproducible statistical results in comparative effectiveness research.

주요 기능

R code snippets for weight estimation and validation
Diagnostic interpretation for Effective Sample Size (ESS)
NICE DSU TSD 18 compliant methodological guidance
0 GitHub stars
Covariate selection and imbalance assessment strategies
Decision framework for anchored vs. unanchored MAIC

사용 사례

Reviewing MAIC analysis results for clinical and statistical consistency
Interpreting low ESS values and adjusting covariate selection to improve validity
Determining if MAIC is the appropriate method for a specific trial dataset

소개

주요 기능

R code snippets for weight estimation and validation
Diagnostic interpretation for Effective Sample Size (ESS)
NICE DSU TSD 18 compliant methodological guidance
0 GitHub stars
Covariate selection and imbalance assessment strategies
Decision framework for anchored vs. unanchored MAIC

사용 사례

Reviewing MAIC analysis results for clinical and statistical consistency
Interpreting low ESS values and adjusting covariate selection to improve validity
Determining if MAIC is the appropriate method for a specific trial dataset