knitr::opts_chunk$set( collapse = TRUE, comment = "#>", message = TRUE, warning = FALSE ) options(knitr.kable.NA = ".") load("mmiss_limit.rda")

This vignette provides up-to-date commands for the analyses in "How to
perform a meta-analysis with R: a practical tutorial", *Evid Based
Ment Health* [@Bald:Ruec:Schw:how:2019].

install.packages(c("meta", "metasens"))

```
library(meta)
```

```
library(metasens)
```

Note, a similar message would be printed for R package
**metasens**. However, this vignette does not actually load
**metasens** as it might not be installed in addition to **meta**.

Print results with two significant digits and use Paule-Mandel estimator for between-study variance.

settings.meta(digits = 2, method.tau = "PM")

Note, in the publication, argument 'method.tau' was used in R function
*metabin()*. Here, we set the Paule-Mandel method as the default for
any meta-analysis conducted in the current R session.

joy = read.csv("Joy2006.txt") # Add new variable: miss joy$miss = ifelse((joy$drop.h + joy$drop.p) == 0, "Without missing data", "With missing data") head(joy) str(joy)

m.publ = metabin(resp.h, resp.h + fail.h, resp.p, resp.p + fail.p, data = joy, studlab = paste0(author, " (", year, ")"), label.e = "Haloperidol", label.c = "Placebo", label.left = "Favours placebo", label.right = "Favours haloperidol")

Print results of meta-analysis (Figure 1).

```
summary(m.publ)
```

Same printout (result not shown)

print(summary(m.publ))

Create Figure 2 (file 'figure2.pdf').

forest(m.publ, sortvar = year, prediction = TRUE, file = "figure2.pdf", width = 10)

knitr::include_graphics("figure2.pdf")

m.publ.sub = update(m.publ, subgroup = miss, print.subgroup.name = FALSE) m.publ.sub

Create Figure 3 (file 'figure3.pdf').

forest(m.publ.sub, sortvar = year, xlim = c(0.1, 100), at = c(0.1, 0.3, 1, 3, 10, 30, 100), test.subgroup.common = FALSE, label.test.subgroup.random = "Test for subgroup differences:", file = "figure3.pdf", width = 10)

knitr::include_graphics("figure3.pdf")

# Impute as no events (ICA-0) - default mmiss.0 = metamiss(m.publ, drop.h, drop.p) # Impute as events (ICA-1) mmiss.1 = metamiss(m.publ, drop.h, drop.p, method = "1") # Observed risk in control group (ICA-pc) mmiss.pc = metamiss(m.publ, drop.h, drop.p, method = "pc") # Observed risk in experimental group (ICA-pe) mmiss.pe = metamiss(m.publ, drop.h, drop.p, method = "pe") # Observed group-specific risks (ICA-p) mmiss.p = metamiss(m.publ, drop.h, drop.p, method = "p") # Best-case scenario (ICA-b) mmiss.b = metamiss(m.publ, drop.h, drop.p, method = "b", small.values = "bad") # Worst-case scenario (ICA-w) mmiss.w = metamiss(m.publ, drop.h, drop.p, method = "w", small.values = "bad") # Gamble-Hollis method mmiss.gh = metamiss(m.publ, drop.h, drop.p, method = "GH") # IMOR.e = 2 and IMOR.c = 2 (same as available case analysis) mmiss.imor2 = metamiss(m.publ, drop.h, drop.p, method = "IMOR", IMOR.e = 2) # IMOR.e = 0.5 and IMOR.c = 0.5 mmiss.imor0.5 = metamiss(m.publ, drop.h, drop.p, method = "IMOR", IMOR.e = 0.5)

Summarise results using R function *metabind()*.

meths = c("Available case analysis (ACA)", "Impute no events (ICA-0)", "Impute events (ICA-1)", "Observed risk in control group (ICA-pc)", "Observed risk in experimental group (ICA-pe)", "Observed group-specific risks (ICA-p)", "Best-case scenario (ICA-b)", "Worst-case scenario (ICA-w)", "Gamble-Hollis analysis", "IMOR.e = 2, IMOR.c = 2", "IMOR.e = 0.5, IMOR.c = 0.5") # Use inverse-variance method for pooling (which is used for # imputation methods) m.publ.iv = update(m.publ, method = "Inverse") # Combine results (random effects) mbr = metabind(m.publ.iv, mmiss.0, mmiss.1, mmiss.pc, mmiss.pe, mmiss.p, mmiss.b, mmiss.w, mmiss.gh, mmiss.imor2, mmiss.imor0.5, name = meths, pooled = "random")

Create Figure 4 (file 'figure4.pdf').

forest(mbr, xlim = c(0.5, 4), leftcols = c("studlab", "I2.w", "tau2.w", "Q.w", "pval.Q.w"), leftlab = c("Meta-Analysis Method", "I2", "Tau2", "Q", "P-value"), type.study = "diamond", digits.addcols = c(4, 2, 2, 2), just.addcols = "right", file = "figure4.pdf", width = 10)

knitr::include_graphics("figure4.pdf")

```
funnel(m.publ)
```

metabias(m.publ, method.bias = "score")

tf.publ = trimfill(m.publ) tf.publ

```
summary(tf.publ)
```

```
funnel(tf.publ)
```

l1.publ = limitmeta(m.publ)

Note, the printout for the limit meta-analysis is not shown in this
vignette as the installation of R package **metasens** is optional.

l1.publ

Create Figure 5 (file 'figure5.pdf').

pdf("figure5.pdf", width = 10, height = 10) # par(mfrow = c(2, 2), pty = "s", oma = c(0, 0, 0, 0), mar = c(4.1, 3.1, 2.1, 1.1)) # funnel(m.publ, xlim = c(0.05, 50), axes = FALSE) axis(1, at = c(0.1, 0.2, 0.5, 1, 2, 5, 10, 50)) axis(2, at = c(0, 0.5, 1, 1.5)) box() title(main = "Panel A: Funnel plot", adj = 0) # funnel(m.publ, xlim = c(0.05, 50), axes = FALSE, contour.levels = c(0.9, 0.95, 0.99), col.contour = c("darkgray", "gray", "lightgray")) legend("topright", c("p < 1%", "1% < p < 5%", "5% < p < 10%", "p > 10%"), fill = c("lightgray", "gray", "darkgray", "white"), border = "white", bg = "white") axis(1, at = c(0.1, 0.2, 0.5, 1, 2, 5, 10, 50)) axis(2, at = c(0, 0.5, 1, 1.5)) box() title(main = "Panel B: Contour-enhanced funnel plot", adj = 0) # funnel(tf.publ, xlim = c(0.05, 50), axes = FALSE) axis(1, at = c(0.1, 0.2, 0.5, 1, 2, 5, 10, 50)) axis(2, at = c(0, 0.5, 1, 1.5)) box() title(main = "Panel C: Trim-and-fill method", adj = 0) # funnel(l1.publ, xlim = c(0.05, 50), axes = FALSE, col.line = 8, lwd.line = 3) axis(1, at = c(0.1, 0.2, 0.5, 1, 2, 5, 10, 50)) axis(2, at = c(0, 0.5, 1, 1.5)) box() title(main = "Panel D: Limit meta-analysis", adj = 0) # dev.off()

knitr::include_graphics("figure5.pdf")

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