What statistical methods does this skill support?

The skill supports interaction tests (group x subgroup), stratified analysis for separate subgroup comparison, and meta-regression for continuous moderators.

Is it capable of identifying confounding variables?

While it can analyze subgroups, it includes specialized warnings regarding the observational nature of subgroup data to ensure users account for potential confounding.

How does it handle multiple testing issues?

It provides built-in recommendations for alpha adjustment, such as the Bonferroni correction, to reduce the risk of Type I error inflation when testing multiple subgroups.

Can I use this for meta-analysis synthesis?

Yes, it is specifically designed to explore heterogeneity and synthesize effect sizes across different study subgroups or continuous moderators.

Subgroup Analysis Pro

Name: Subgroup Analysis Pro
Author: astoreyai

byastoreyai

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データサイエンスとML

Conducts rigorous statistical subgroup analyses to identify effect moderation and data heterogeneity across diverse research populations.

概要

This skill empowers Claude to perform sophisticated subgroup analyses, enabling researchers to identify how treatment effects or observations vary across different populations or study conditions. It automates the process of testing pre-specified moderators, calculating interaction effects, and performing meta-regressions. By incorporating best practices like Bonferroni corrections and interaction tests, the skill helps prevent Type I error inflation and ensures that exploratory findings are interpreted with the necessary statistical caution.

主な機能

Stratified analysis for comparative effect size reporting
Integrated multiple comparison adjustments including Bonferroni corrections
Automated interaction tests to measure group-subgroup variables
Meta-regression support for continuous and multiple moderators
Expert guidance on interpreting observational data vs. randomized subgroups
2 GitHub stars

ユースケース

Analyzing clinical trial data to determine if drug efficacy differs by age, sex, or severity
Identifying differential effects in social science research or longitudinal studies
Exploring heterogeneity in meta-analyses across multiple research papers or datasets

概要

主な機能

Stratified analysis for comparative effect size reporting
Integrated multiple comparison adjustments including Bonferroni corrections
Automated interaction tests to measure group-subgroup variables
Meta-regression support for continuous and multiple moderators
Expert guidance on interpreting observational data vs. randomized subgroups
2 GitHub stars

ユースケース

Analyzing clinical trial data to determine if drug efficacy differs by age, sex, or severity
Identifying differential effects in social science research or longitudinal studies
Exploring heterogeneity in meta-analyses across multiple research papers or datasets