Does this skill support binary outcomes like Odds Ratios?

Yes, it provides implementation patterns for Odds Ratios (OR), Risk Ratios (RR), and Risk Differences (RD) using the escalc and metabin functions.

Is Bayesian meta-analysis supported?

Yes, it includes templates for fitting Bayesian random-effects meta-analysis models using the brms package with custom priors.

Can I perform subgroup analysis or meta-regression?

Yes, the skill covers subgroup analysis for categorical moderators and meta-regression for continuous or mixed moderators using the rma function.

How does the skill handle publication bias?

It includes methods for creating contour-enhanced funnel plots, performing Egger's regression and Begg's rank correlation tests, and applying the trim-and-fill method.

Which R packages are utilized for these meta-analysis methods?

The skill primarily utilizes the 'metafor' and 'meta' packages for frequentist analysis, and 'brms' for Bayesian meta-analysis.

Meta-Analysis Methods in R

Name: Meta-Analysis Methods in R
Author: choxos

bychoxos

Data Science & ML

Implements comprehensive meta-analysis workflows in R, including effect size calculation, heterogeneity assessment, and publication bias detection.

About

This skill provides a robust framework for conducting pairwise meta-analyses using leading R packages like metafor and meta. It covers the complete evidence synthesis pipeline, from calculating effect sizes for continuous, binary, and survival data to fitting complex fixed and random effects models. Designed for researchers and data scientists, it streamlines advanced statistical tasks such as meta-regression, sensitivity analysis, and Bayesian modeling, while offering high-quality visualization patterns for forest and funnel plots.

Key Features

Fixed and random effects modeling with multiple tau estimators
Advanced heterogeneity diagnostics including I-squared and prediction intervals
Comprehensive effect size calculation (SMD, OR, RR, HR, ZCOR)
Publication bias detection using Egger's regression and trim-and-fill methods
0 GitHub stars
Customizable forest, funnel, and Baujat plots for evidence visualization

Use Cases

Performing meta-regressions to identify study-level moderators of heterogeneity
Synthesizing clinical trial data to determine overall treatment efficacy
Evaluating potential publication bias and small-study effects in research literature

About

Key Features

Fixed and random effects modeling with multiple tau estimators
Advanced heterogeneity diagnostics including I-squared and prediction intervals
Comprehensive effect size calculation (SMD, OR, RR, HR, ZCOR)
Publication bias detection using Egger's regression and trim-and-fill methods
0 GitHub stars
Customizable forest, funnel, and Baujat plots for evidence visualization

Use Cases

Performing meta-regressions to identify study-level moderators of heterogeneity
Synthesizing clinical trial data to determine overall treatment efficacy
Evaluating potential publication bias and small-study effects in research literature