Easy Forest Plots in R

Forest plots are great ways to visualize individual group estimates as well as investigate heterogeneity of effect.  Most forest plot programs will display combined effect estimates and give you an indicator of whether there is evidence for heterogeneity among subgroups.  Fortunately, the R metafor package makes meta-analysis and plotting forest plots relatively easy.  Below is a sample R script that will combine beta and standard error estimates from an input file (input.txt) and create simple forest plots with an overall estimate as well as p-values for association and heterogeneity.

In general, the input.txt file should either

(1) Have the columns:
group - name for the study or group
beta - the log odds ratio for the effect of interest
se - the standard error for the log odds ratio estimate

or

(2) Have the columns:
group - name for the study or group
OR - odds ratio for the effect of interest
LCL - lower confidence interval for the odds ratio
UCL - upper confidence interval for the odds ratio

For the second case where you have an odds ratio and 95% confidence estimates, beta and se need to be estimated.  This is done by uncommenting lines 8 and 9 of the script.  Of note: due to rounding error the final forest plot may have 95% CI limits that are one digit off.

The R script that uses the metafor package as well as an example input.txt are below.

	# Load metafor library
	library(metafor)
	# Load input.tyx
	data <- read.table("input.txt",as.is=T, header=T, sep="\t")
	# # If OR's and 95% CI's, run these commented out lines
	# data$beta <- log(data$OR)
	# data$se <- (log(data$UCL)-log(data$LCL))/(2*1.96)
	# Assign values for plotting
	labs <- data$group
	yi <- data$beta
	sei <- data$se
	# Combine data into summary estimate
	res <- rma(yi=yi, sei=sei, method="FE")
	summary(res)
	# Plot combined data
	forest(res, transf=exp, refline=1, xlab="Odds Ratio (95%CI)", slab=labs, mlab="Summary Estimate")
	mtext(paste("Association p-value=",summary(res)$pval),side=3, line=-1)
	mtext(paste("Heterogeneity p-value=",summary(res)$QEp),side=3, line=-2.25)

view raw forest_plot.R hosted with ❤ by GitHub

	group beta se
	Study 01 0.635422995842 0.239880139026
	Study 02 -0.043430833437 0.255182175384
	Study 03 0.808777362855 0.278587375378
	Study 04 0.233386044408 0.316660371211
	Study 05 0.237572509459 0.195872577686
	Study 06 0.541210360393 0.264106181227
	Study 07 0.201281009793 0.179269971554
	Study 08 -0.009615458699 0.443981914977
	Study 09 0.569423560036 0.127545364349
	Study 10 0.673278977343 0.293661994161

view raw input.txt hosted with ❤ by GitHub