meta-analysislisted

# Meta-Analysis Methods in R ## Overview Comprehensive pairwise meta-analysis methods covering effect size calculation, fixed and random effects models, heterogeneity assessment, publication bias detection, subgroup analysis, meta-regression, and sensitivity analyses for synthesizing evidence across studies. ## Effect Size Calculation ### Continuous Outcomes ```r library(metafor) # Standardized mean difference (SMD/Cohen's d/Hedges' g) dat <- escalc( measure = "SMD", # Hedges' g (bias-corrected) m1i = mean_treatment, # Treatment group mean sd1i = sd_treatment, # Treatment group SD n1i = n_treatment, # Treatment group n m2i = mean_control, # Control group mean sd2i = sd_control, # Control group SD n2i = n_control, # Control group n data = studies ) # Mean difference (unstandardized) dat_md <- escalc( measure = "MD", m1i = mean_treatment, m2i = mean_control, sd1i = sd_treatment, sd2i = sd_control, n1i = n_treatment, n2i = n_control, data = studies ) # From pre-computed means and SEs dat_pre <- escalc( measure = "SMD", yi = effect_size, # Pre-computed effect sei = standard_error, # Standard error data = studies ) ``` ### Binary Outcomes ```r library(metafor) # Odds ratio dat_or <- escalc( measure = "OR", ai = events_treatment, # Events in treatment bi = n_treatment - events_treatment, # Non-events treatment ci = events_control, # Events in control di = n_control - events_control, # Non-ev