Does it cover publication bias?

Absolutely. It includes guidance on visual assessment via funnel plots and statistical tests such as Egger’s regression, Peters’ test, and Begg’s rank test.

Can it generate R code for meta-analysis?

Yes, it provides ready-to-use R snippets for popular packages like 'meta' and 'metafor', including advanced settings like Knapp-Hartung adjustments and prediction intervals.

What is the importance of prediction intervals in this skill?

This skill emphasizes prediction intervals (PI) alongside confidence intervals (CI) to show the range where 95% of true study effects lie, which is critical for clinical decision-making.

What reporting standards does it follow?

The skill is strictly aligned with the Cochrane Handbook for Systematic Reviews and the PRISMA reporting statement to ensure clinical research quality.

How does this skill help with model selection?

It provides a decision framework to help you choose between fixed-effect and random-effects models based on study similarity, population diversity, and the intended scope of inference.

Pairwise Meta-Analysis Methodology

Name: Pairwise Meta-Analysis Methodology
Author: choxos

bychoxos

0•

데이터 과학 및 ML

Provides expert methodological guidance and R code implementation for conducting rigorous pairwise meta-analyses following Cochrane and PRISMA standards.

This skill equips Claude with deep domain knowledge for pairwise meta-analysis, enabling it to assist researchers in selecting statistical models, interpreting heterogeneity (I², Q, τ²), assessing publication bias via funnel plots and Egger’s tests, and performing sensitivity analyses like leave-one-out and GOSH. It bridges the gap between theoretical biostatistics and practical implementation in R using packages like 'meta' and 'metafor', ensuring that systematic reviews meet high-quality reporting standards and avoid common pitfalls like over-interpreting I² or ignoring prediction intervals.

주요 기능

01Comprehensive heterogeneity assessment (I², Cochran’s Q, Tau-squared, and Prediction Intervals)

02Statistical model selection (Fixed-effect vs. Random-effects decision framework)

030 GitHub stars

04Publication bias detection using Funnel plots, Egger’s, and Peters’ tests

05Standardized reporting checklists based on PRISMA and Cochrane guidelines

06Advanced sensitivity analysis including Leave-one-out, Influence diagnostics, and GOSH analysis

사용 사례

01Conducting sensitivity analyses to validate the robustness of pooled effect estimates

02Planning and protocol development for systematic reviews and meta-analyses

03Interpreting complex statistical outputs and choosing appropriate reporting metrics

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent pairwise-ma-methodology

For use in Claude.ai and ChatGPT

주요 기능

01Comprehensive heterogeneity assessment (I², Cochran’s Q, Tau-squared, and Prediction Intervals)

02Statistical model selection (Fixed-effect vs. Random-effects decision framework)

030 GitHub stars

04Publication bias detection using Funnel plots, Egger’s, and Peters’ tests

05Standardized reporting checklists based on PRISMA and Cochrane guidelines

06Advanced sensitivity analysis including Leave-one-out, Influence diagnostics, and GOSH analysis

사용 사례

01Conducting sensitivity analyses to validate the robustness of pooled effect estimates

02Planning and protocol development for systematic reviews and meta-analyses

03Interpreting complex statistical outputs and choosing appropriate reporting metrics