plots:forest_plot_with_subgroups
Table of Contents
Forest Plot with Subgroups
Description
Below is an example of a forest plot with three subgroups. The results of the individual studies are shown grouped together according to their subgroup. Below each subgroup, a summary polygon shows the results when fitting a random-effects model just to the studies within that group. The summary polygon at the bottom of the plot shows the results from a random-effects model when analyzing all 13 studies.
Plot
Code
library(metafor) ### copy BCG vaccine meta-analysis data into 'dat' dat <- dat.bcg ### calculate log risk ratios and corresponding sampling variances (and use ### the 'slab' argument to store study labels as part of the data frame) dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat, slab=paste(author, year, sep=", ")) ### fit random-effects model res <- rma(yi, vi, data=dat) ### a little helper function to add Q-test, I^2, and tau^2 estimate info mlabfun <- function(text, res) { list(bquote(paste(.(text), " (Q = ", .(formatC(res$QE, digits=2, format="f")), ", df = ", .(res$k - res$p), ", p ", .(metafor:::.pval(res$QEp, digits=2, showeq=TRUE, sep=" ")), "; ", I^2, " = ", .(formatC(res$I2, digits=1, format="f")), "%, ", tau^2, " = ", .(formatC(res$tau2, digits=2, format="f")), ")")))} ### set up forest plot (with 2x2 table counts added; the 'rows' argument is ### used to specify in which rows the outcomes will be plotted) forest(res, xlim=c(-16, 4.6), at=log(c(0.05, 0.25, 1, 4)), atransf=exp, ilab=cbind(tpos, tneg, cpos, cneg), ilab.xpos=c(-9.5,-8,-6,-4.5), cex=0.75, ylim=c(-1, 27), order=alloc, rows=c(3:4,9:15,20:23), mlab=mlabfun("RE Model for All Studies", res), psize=1, header="Author(s) and Year") ### set font expansion factor (as in forest() above) and use a bold font op <- par(cex=0.75, font=2) ### add additional column headings to the plot text(c(-9.5,-8,-6,-4.5), 26, c("TB+", "TB-", "TB+", "TB-")) text(c(-8.75,-5.25), 27, c("Vaccinated", "Control")) ### switch to bold italic font par(font=4) ### add text for the subgroups text(-16, c(24,16,5), pos=4, c("Systematic Allocation", "Random Allocation", "Alternate Allocation")) ### set par back to the original settings par(op) ### fit random-effects model in the three subgroups res.s <- rma(yi, vi, subset=(alloc=="systematic"), data=dat) res.r <- rma(yi, vi, subset=(alloc=="random"), data=dat) res.a <- rma(yi, vi, subset=(alloc=="alternate"), data=dat) ### add summary polygons for the three subgroups addpoly(res.s, row=18.5, mlab=mlabfun("RE Model for Subgroup", res.s)) addpoly(res.r, row= 7.5, mlab=mlabfun("RE Model for Subgroup", res.r)) addpoly(res.a, row= 1.5, mlab=mlabfun("RE Model for Subgroup", res.a)) ### fit meta-regression model to test for subgroup differences res <- rma(yi, vi, mods = ~ alloc, data=dat) ### add text for the test of subgroup differences text(-16, -1.8, pos=4, cex=0.75, bquote(paste("Test for Subgroup Differences: ", Q[M], " = ", .(formatC(res$QM, digits=2, format="f")), ", df = ", .(res$p - 1), ", p = ", .(formatC(res$QMp, digits=2, format="f")))))
plots/forest_plot_with_subgroups.txt ยท Last modified: 2022/04/22 11:17 by Wolfgang Viechtbauer