Baujat plot to explore heterogeneity in meta-analysis

Share:

Description

Draw a Baujat plot to explore heterogeneity in meta-analysis.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
baujat(x, ...)

## S3 method for class 'meta'
baujat(x,
       yscale=1, xlim, ylim,
       xlab="Contribution to overall heterogeneity",
       ylab="Influence on overall result",
       pch=21, cex=1, col="black", bg="darkgray",
       studlab=TRUE, cex.studlab=0.8,
       xmin=0, ymin=0, pos=2, offset=0.5,
       grid=TRUE, col.grid="lightgray", lty.grid="dotted", lwd.grid=par("lwd"),
       pty="s", ...)

Arguments

x

An object of class meta.

yscale

Scaling factor for values on y-axis.

xlim

The x limits (min,max) of the plot.

ylim

The y limits (min,max) of the plot.

xlab

A label for the x-axis.

ylab

A label for the y-axis.

pch

The plotting symbol used for individual studies.

cex

The magnification to be used for plotting symbol.

col

A vector with colour of plotting symbols.

bg

A vector with background colour of plotting symbols (only used if pch in 21:25).

studlab

A logical indicating whether study labels should be printed in the graph. A vector with study labels can also be provided (must be of same length as x$TE then).

cex.studlab

The magnification for study labels.

xmin

A numeric specifying minimal value to print study labels (on x-axis).

ymin

A numeric specifying minimal value to print study labels (on y-axis).

pos

A position specifier for study labels (see text).

offset

Offset for study labels (see text).

grid

A logical indicating whether a grid is printed in the plot.

col.grid

Colour for grid lines.

lty.grid

The line type for grid lines.

lwd.grid

The line width for grid lines.

pty

A character specifying type of plot region (see par).

...

Graphical arguments as in par may also be passed as arguments.

Details

Baujat et al. (2002) introduced a scatter plot to explore heterogeneity in meta-analysis. On the x-axis the contribution of each study to the overall heterogeneity statistic (see list object Q of the meta-analysis object x) is plotted. On the y-axis the standardised difference of the overall treatment effect with and without each study is plotted; this quantity describes the influence of each study on the overal treatment effect.

Internally, the metainf function is used to calculate the values on the y-axis.

Value

A data.frame with the following variables:

x

Coordinate on x-axis (contribution to heterogeneity statistic).

y

Coordinate on y-axis (influence on overall treatment effect).

Author(s)

Guido Schwarzer sc@imbi.uni-freiburg.de

References

Baujat B, Mahé C, Pignon JP, Hill C (2002), A graphical method for exploring heterogeneity in meta-analyses: Application to a meta-analysis of 65 trials. Statistics in Medicine, 30, 2641–2652.

See Also

metagen, metainf

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
data(Olkin95)

m1 <- metabin(event.e, n.e, event.c, n.c, data=Olkin95,
              studlab=author, sm="OR", method="I")

# Generate Baujat plot
baujat(m1)

# Do not print study labels if the x-value is smaller than 4 and the
# y-value is smaller than 1.
baujat(m1, yscale=10, xmin=4, ymin=1)

# Change position of study labels
baujat(m1, yscale=10, xmin=4, ymin=1,
       pos=1, xlim=c(0, 6.5))

# Generate Baujat plot and assign x- and y- coordinates to R object b1
b1 <- baujat(m1)

# Calculate overall heterogeneity statistic
sum(b1$x)
m1$Q

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.