bayesmeta (version 2.3)

forestplot.bayesmeta: Generate a forest plot for a bayesmeta object (based on the forestplot package's plotting functions).

Description

Generates a forest plot, showing individual estimates along with their 95 percent confidence intervals, shrinkage intervals, resulting effect estimate and prediction interval.

Usage

# S3 method for bayesmeta
forestplot(x, labeltext, exponentiate=FALSE,
           prediction=TRUE, shrinkage=TRUE, digits=2, plot=TRUE,
           fn.ci_norm, fn.ci_sum, col, legend, boxsize, ...)

Arguments

x

a bayesmeta object.

labeltext

an (alternative) “labeltext” argument which is then handed on to the forestplot() function (see the help there). You can use this to change contents or add columns to the displayed table; see the example below.

exponentiate

a logical flag indicating whether to exponentiate numbers (effect sizes) in table and plot.

prediction

a logical flag indicating whether to show the prediction interval below the mean estimate.

shrinkage

a logical flag indicating whether to show shrinkage intervals along with the quoted estimates.

digits

The number of significant digits to be shown. This is interpreted relative to the standard errors of all estimates.

plot

a logical flag indicating whether to actually generate a plot.

fn.ci_norm, fn.ci_sum, col, legend, boxsize, …

other arguments passed on to the forestplot package's forestplot function (see also the help there).

Details

Generates a forest plot illustrating the underlying data and resulting estimates (effect estimate and prediction interval, as well as shrinkage estimates and intervals).

References

C. Roever. Bayesian random-effects meta-analysis using the bayesmeta R package. arXiv preprint 1711.08683 (submitted for publication), 2017.

C. Lewis and M. Clarke. Forest plots: trying to see the wood and the trees. BMJ, 322:1479, 2001.

C. Guddat, U. Grouven, R. Bender and G. Skipka. A note on the graphical presentation of prediction intervals in random-effects meta-analyses. Systematic Reviews, 1(34), 2012.

R.D. Riley, J.P. Higgins and J.J. Deeks. Interpretation of random effects meta-analyses. BMJ, 342:d549, 2011.

See Also

bayesmeta, forestplot, forest.bayesmeta, plot.bayesmeta.

Examples

# NOT RUN {
# load data:
data("CrinsEtAl2014")

# }
# NOT RUN {
# compute effect sizes (log odds ratios) from count data
# (using "metafor" package's "escalc()" function):
require("metafor")
crins.es <- escalc(measure="OR",
                   ai=exp.AR.events,  n1i=exp.total,
                   ci=cont.AR.events, n2i=cont.total,
                   slab=publication, data=CrinsEtAl2014)
print(crins.es)

# perform meta analysis:
crins.ma <- bayesmeta(crins.es, tau.prior=function(t){dhalfcauchy(t,scale=1)})

########################
# generate forest plots
require("forestplot")

# default options:
forestplot(crins.ma)

# exponentiate values (shown in table and plot), show vertical line at OR=1:
forestplot(crins.ma, expo=TRUE, zero=1)

# logarithmic x-axis:
forestplot(crins.ma, expo=TRUE, xlog=TRUE)

# omit prediction interval:
forestplot(crins.ma, predict=FALSE)

# omit shrinkage intervals:
forestplot(crins.ma, shrink=FALSE)

# show more decimal places:
forestplot(crins.ma, digits=3)

# change table values:
# (here: add columns for event counts)
fp <- forestplot(crins.ma, expo=TRUE, plot=FALSE)
labtext <- fp$labeltext
labtext <- cbind(labtext[,1],
                 c("treatment",
                   paste0(CrinsEtAl2014[,"exp.AR.events"], "/", CrinsEtAl2014[,"exp.total"]),
                   "",""),
                 c("control",
                   paste0(CrinsEtAl2014[,"cont.AR.events"], "/", CrinsEtAl2014[,"cont.total"]),
                   "",""),
                 labtext[,2:3])
labtext[1,4] <- "OR"
print(fp$labeltext) # before
print(labtext)      # after
forestplot(crins.ma, labeltext=labtext, expo=TRUE, xlog=TRUE)

# see also the "forestplot" help for more arguments that you may change,
# e.g. the "clip", "xticks", "xlab" and "title" arguments,
# or the "txt_gp" argument for label sizes etc.:
forestplot(crins.ma, clip=c(-4,1), xticks=(-3):0,
           xlab="log-OR", title="pediatric transplantation example",
           txt_gp = fpTxtGp(ticks = gpar(cex=1), xlab = gpar(cex=1)))
# }