#THIS IS AN R CODE SCRIPT

library(rstudioapi)
library(readxl)
library(meta)

# Select Excel file
filepath <- "E:/SAVE R FILES HERE/Meta-analysis/Pip-tazo vs Cefepime/Extracted data.xlsx"

# Read sheets
sheetsnames <- excel_sheets(filepath)
ma <- lapply(sheetsnames, function(x) {
  as.data.frame(read_excel(filepath, sheet = x))
})
names(ma) <- sheetsnames

# Use your continuous dataset
data <- ma$Duration_of_treatment

# Run meta-analysis (FIXED column names)
m.cont <- metacont(
  n.e    = n_interv,
  mean.e = mean_interv,
  sd.e   = sd_interv,
  n.c    = n_ctrl,
  mean.c = mean_ctrl,
  sd.c   = sd_ctrl,
  data   = data,
  studlab = Authors,
  sm = "MD",
  method.tau = "REML",
  method.random.ci = "classic",
  random = TRUE,
  common = FALSE,
  prediction = FALSE
)

summary(m.cont)


png(
  filename = "E:/SAVE R FILES HERE/Meta-analysis/Pip-tazo vs Cefepime/SUCCESS FOREST PLOT.png",
  width = 5000,   # wide enough for long study names
  height = 1500,
  res = 300
)


forest(m.cont,
       layout = "Revman",
       random = T,
       common = F,
       test.overall.random = T,
       pooled.events = T,
       label.e = "Pip-Tazo",
       label.c = "Cefepime",
       label.left = "Favors Pip-Tazo",
       label.right = "Favors Cefepime",
       leftcols = c("studlab",
                    "mean.e","sd.e","n.e",
                    "mean.c","sd.c","n.c",
                    "w.random",
                    "effect","ci"),
       leftlabs = c("Study", 
                    NA, NA,NA, 
                    NA, NA,NA,
                    "Weight (%)", 
                    "Effect size", "95% CI"),
       
       xlim = c(-5, 6),
       ff.xlab = "bold",
       col.square = "darkblue",
       col.square.lines = "black",
       col.diamond.random = "white",
       col.diamond.lines.random = "black",
       colgap = "5mm",
       colgap.studlab = "10mm",
       colgap.forest.left = "5mm",
       just = "center",
       digits = 1,
       digits.mean = 1,
       digits.sd = 1,
       digits.pval = 1)

dev.off()