meta (version 6.5-0)

baujat.meta: Baujat plot to explore heterogeneity in meta-analysis

Description

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

Usage

# S3 method for 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,
  pos.studlab,
  offset = 0.5,
  xmin = 0,
  ymin = 0,
  grid = TRUE,
  col.grid = "lightgray",
  lty.grid = "dotted",
  lwd.grid = par("lwd"),
  pty = "s",
  pooled,
  ...
)

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)

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.

pos.studlab

Position of study labels, see argument pos in text.

offset

Offset for study labels (see text).

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).

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).

pooled

A character string indicating whether a common effect or random effects model is used for pooling. Either missing (see Details), "common" or "random", can be abbreviated.

...

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

Author

Guido Schwarzer

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.

Information from object x is utilised if argument pooled is missing. A common effect model is assumed (pooled="common") if argument x$common is TRUE; a random effects model is assumed (pooled="random") if argument x$random is TRUE and x$common is FALSE.

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

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--52

See Also

metagen, metainf

Examples

data(Olkin1995)

# Only consider first ten studies
m1 <- metabin(ev.exp, n.exp, ev.cont, n.cont,
  data = Olkin1995, sm = "OR", method = "I", studlab = paste(author, year),
  subset = 1:10)

# Generate Baujat plot
baujat(m1)

if (FALSE) {
m1 <- metabin(ev.exp, n.exp, ev.cont, n.cont,
  data = Olkin1995, sm = "OR", method = "I", studlab = paste(author, year))

# 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
}