Text
A forest plot is closely connected to text and the ability to customize the text is central.
Table of text
Below is a basic example from the original forestplot function that shows how to use a table of text:
library(forestplot)
# Cochrane data from the 'rmeta'-package
cochrane_from_rmeta <-
structure(list(
mean = c(NA, NA, 0.578, 0.165, 0.246, 0.700, 0.348, 0.139, 1.017, NA, 0.531),
lower = c(NA, NA, 0.372, 0.018, 0.072, 0.333, 0.083, 0.016, 0.365, NA, 0.386),
upper = c(NA, NA, 0.898, 1.517, 0.833, 1.474, 1.455, 1.209, 2.831, NA, 0.731)),
.Names = c("mean", "lower", "upper"),
row.names = c(NA, -11L),
class = "data.frame")
tabletext<-cbind(
c("", "Study", "Auckland", "Block",
"Doran", "Gamsu", "Morrison", "Papageorgiou",
"Tauesch", NA, "Summary"),
c("Deaths", "(steroid)", "36", "1",
"4", "14", "3", "1",
"8", NA, NA),
c("Deaths", "(placebo)", "60", "5",
"11", "20", "7", "7",
"10", NA, NA),
c("", "OR", "0.58", "0.16",
"0.25", "0.70", "0.35", "0.14",
"1.02", NA, "0.53"))
forestplot(tabletext,
cochrane_from_rmeta,new_page = TRUE,
is.summary=c(TRUE,TRUE,rep(FALSE,8),TRUE),
clip=c(0.1,2.5),
xlog=TRUE,
col=fpColors(box="royalblue",line="darkblue", summary="royalblue"))
Summary lines
The same as above but with lines based on the summary elements
forestplot(tabletext,
hrzl_lines = gpar(col="#444444"),
cochrane_from_rmeta,new_page = TRUE,
is.summary=c(TRUE,TRUE,rep(FALSE,8),TRUE),
clip=c(0.1,2.5),
xlog=TRUE,
col=fpColors(box="royalblue",line="darkblue", summary="royalblue"))
We can also choose what lines we want by provifing a list where the name is the line number affected, in the example below 3rd line and 11th counting the first line to be above the first row (not that there is an empty row before summary):
forestplot(tabletext,
hrzl_lines = list("3" = gpar(lty=2),
"11" = gpar(lwd=1, columns=1:4, col = "#000044")),
cochrane_from_rmeta,new_page = TRUE,
is.summary=c(TRUE,TRUE,rep(FALSE,8),TRUE),
clip=c(0.1,2.5),
xlog=TRUE,
col=fpColors(box="royalblue",line="darkblue", summary="royalblue", hrz_lines = "#444444"))
Positioning the graph element
You can also choose to have the graph positioned within the text table by specifying the graph.pos argument:
forestplot(tabletext,
graph.pos = 4,
hrzl_lines = list("3" = gpar(lty=2),
"11" = gpar(lwd=1, columns=c(1:3,5), col = "#000044"),
"12" = gpar(lwd=1, lty=2, columns=c(1:3,5), col = "#000044")),
cochrane_from_rmeta,new_page = TRUE,
is.summary=c(TRUE,TRUE,rep(FALSE,8),TRUE),
clip=c(0.1,2.5),
xlog=TRUE,
col=fpColors(box="royalblue",line="darkblue", summary="royalblue", hrz_lines = "#444444"))
Using expressions
If we present a regression output it is sometimes convenient to have non-ascii letters. We will use my study comparing health related quality of life 1 year after total hip arthroplasties between Sweden and Denmark for this section:
data(HRQoL)
clrs <- fpColors(box="royalblue",line="darkblue", summary="royalblue")
tabletext <-
list(c(NA, rownames(HRQoL$Sweden)),
append(list(expression(beta)), sprintf("%.2f", HRQoL$Sweden[,"coef"])))
forestplot(tabletext, new_page = TRUE,
rbind(rep(NA, 3),
HRQoL$Sweden),
col=clrs,
xlab="EQ-5D index")
Altering fonts
Altering fonts may give a completely different feel to the table:
tabletext <- cbind(rownames(HRQoL$Sweden),
sprintf("%.2f", HRQoL$Sweden[,"coef"]))
forestplot(tabletext,
txt_gp = fpTxtGp(label = gpar(fontfamily = "HersheyScript")),
rbind(HRQoL$Sweden),
col=clrs,
xlab="EQ-5D index")
There is also the possibility of being selective in gp-styles:
forestplot(tabletext,
txt_gp = fpTxtGp(label = list(gpar(fontfamily = "HersheyScript"),
gpar(fontfamily = "",
col = "#660000")),
ticks = gpar(fontfamily = "", cex=1),
xlab = gpar(fontfamily = "HersheySerif", cex = 1.5)),
rbind(HRQoL$Sweden),
col=clrs,
xlab="EQ-5D index")
Confidence intervals
Clipping the interval is convenient for uncertain estimates in order to retain the resolution for those of more interest. The clipping simply adds an arrow to the confidence interval, see the bottom estimate below:
forestplot(tabletext,
rbind(HRQoL$Sweden),
clip =c(-.1, Inf),
col=clrs,
xlab="EQ-5D index")
Multiple confidence bands
When combining similar outcomes for the same exposure I’ve found it useful to use multiple bands per row. This efficiently increases the data-ink ratio while making the comparison between the two bands trivial. The first time I’ve used this was in my article comparing Swedish with Danish patients 1 year after total hip arthroplasty. Here the clipping also becomes obvious as the Danish sample was much smaller, resulting in wider confidence intervals.
tabletext <- tabletext[,1]
forestplot(tabletext,
mean = cbind(HRQoL$Sweden[, "coef"], HRQoL$Denmark[, "coef"]),
lower = cbind(HRQoL$Sweden[, "lower"], HRQoL$Denmark[, "lower"]),
upper = cbind(HRQoL$Sweden[, "upper"], HRQoL$Denmark[, "upper"]),
clip =c(-.1, 0.075),
col=fpColors(box=c("blue", "darkred")),
xlab="EQ-5D index")
Estimate indicator
You can choose between a number of different estimate indicators. Using the example above we can set the Danish results to circles.
forestplot(tabletext,
fn.ci_norm = c(fpDrawNormalCI, fpDrawCircleCI),
boxsize = .25, # We set the box size to better visualize the type
line.margin = .1, # We need to add this to avoid crowding
mean = cbind(HRQoL$Sweden[, "coef"], HRQoL$Denmark[, "coef"]),
lower = cbind(HRQoL$Sweden[, "lower"], HRQoL$Denmark[, "lower"]),
upper = cbind(HRQoL$Sweden[, "upper"], HRQoL$Denmark[, "upper"]),
clip =c(-.125, 0.075),
col=fpColors(box=c("blue", "darkred")),
xlab="EQ-5D index")
The confidence interval/box drawing functions are fully customizeable. You can write your own function that accepts the parameters: lower_limit, estimate, upper_limit, size, y.offset, clr.line, clr.marker, and lwd.
