network-meta-analysislisted

# Network Meta-Analysis in R ## Overview Network meta-analysis (NMA) methods for comparing multiple treatments simultaneously using direct and indirect evidence. Covers network structure assessment, frequentist and Bayesian NMA approaches, consistency evaluation, treatment rankings, and visualization techniques. ## Network Structure and Data Preparation ### Pairwise Data Format ```r library(netmeta) # Standard pairwise format for contrast-based NMA pairwise_data <- data.frame( study = c("Study1", "Study1", "Study2", "Study2", "Study3", "Study3", "Study4", "Study4", "Study5", "Study5", "Study5"), treat1 = c("A", "A", "A", "B", "B", "B", "A", "C", "A", "B", "C"), treat2 = c("B", "C", "B", "C", "C", "D", "C", "D", "B", "C", "D"), TE = c(0.5, 0.3, 0.4, -0.2, 0.1, 0.3, 0.35, 0.25, 0.45, -0.15, 0.2), seTE = c(0.1, 0.12, 0.11, 0.13, 0.15, 0.14, 0.09, 0.11, 0.10, 0.12, 0.13) ) # Create network meta-analysis object net <- netmeta( TE = TE, seTE = seTE, treat1 = treat1, treat2 = treat2, studlab = study, data = pairwise_data, sm = "MD", # Effect measure reference.group = "A", # Reference treatment all.treatments = NULL # Auto-detect ) summary(net) ``` ### Arm-Level Data Format ```r library(netmeta) # Convert arm-level to pairwise format arm_data <- data.frame( study = rep(c("S1", "S2", "S3"), c(2, 3, 2)), treatment = c("A", "B", "A", "B", "C", "B", "C"), n = c(50, 52, 48, 51, 49, 55, 53), events