pairwise-ma-methodologylisted

# Pairwise Meta-Analysis Methodology Comprehensive methodological guidance for conducting rigorous pairwise meta-analysis following Cochrane and PRISMA guidelines. ## When to Use This Skill - Planning a pairwise meta-analysis - Choosing between fixed and random effects models - Interpreting heterogeneity statistics - Assessing publication bias - Designing sensitivity analyses - Reviewing pairwise MA code or results ## Fixed vs Random Effects ### Decision Framework ``` Are studies functionally identical? ├── Yes → Fixed-effect model appropriate │ - Same population, intervention, comparator, outcome │ - Estimating single "true" effect │ └── No (usually the case) → Random-effects model - Studies differ in ways that affect true effect - Estimating mean of distribution of effects - More generalizable inference ``` ### When to Use Fixed-Effect - Studies are very similar (rare in practice) - Want to estimate effect in "identical" studies - Very few studies (< 5) - random effects unreliable - Sensitivity analysis alongside random effects ### When to Use Random-Effects - Studies differ in populations, settings, methods - Want inference applicable beyond included studies - Default choice for most meta-analyses - Use with appropriate adjustments (Knapp-Hartung) ### Key Differences | Aspect | Fixed-Effect | Random-Effects | |--------|-------------|----------------| | Assumption | Common true effect | Distribution of true effects | | Weights | Based on precisio