R: Forest Plots (Method for 'cumul.rma' Objects)

forest.cumul.rma {metafor}

R Documentation

Forest Plots (Method for 'cumul.rma' Objects)

Description

Function to create forest plots for objects of class "cumul.rma".

Usage

## S3 method for class 'cumul.rma'
forest(x, annotate=TRUE, header=FALSE,
       xlim, alim, olim, ylim, at, steps=5,
       level=x$level, refline=0, digits=2L, width,
       xlab, ilab, ilab.xpos, ilab.pos,
       transf, atransf, targs, rows,
       efac=1, pch, psize, col, shade, colshade,
       lty, fonts, cex, cex.lab, cex.axis, ...)

Arguments

`x`	an object of class `"cumul.rma"` obtained with `cumul`.
`annotate`	logical to specify whether annotations should be added to the plot (the default is `TRUE`).
`header`	logical to specify whether column headings should be added to the plot (the default is `FALSE`). Can also be a character vector to specify the left and right headings (or only the left one).
`xlim`	horizontal limits of the plot region. If unspecified, the function sets the horizontal plot limits to some sensible values.
`alim`	the x-axis limits. If unspecified, the function sets the x-axis limits to some sensible values.
`olim`	optional argument to specify observation/outcome limits. If unspecified, no limits are used.
`ylim`	the y-axis limits of the plot. If unspecified, the function sets the y-axis limits to some sensible values. Can also be a single value to set the lower bound (while the upper bound is still set automatically).
`at`	position of the x-axis tick marks and corresponding labels. If unspecified, the function sets the tick mark positions/labels to some sensible values.
`steps`	the number of tick marks for the x-axis (the default is 5). Ignored when the positions are specified via the `at` argument.
`level`	numeric value between 0 and 100 to specify the confidence interval level (see here for details). The default is to take the value from the object.
`refline`	numeric value to specify the location of the vertical ‘reference’ line (the default is 0). The line can be suppressed by setting this argument to `NA`.
`digits`	integer to specify the number of decimal places to which the tick mark labels of the x-axis and the annotations should be rounded (the default is `2L`). Can also be a vector of two integers, the first to specify the number of decimal places for the annotations, the second for the x-axis labels. When specifying an integer (e.g., `2L`), trailing zeros after the decimal mark are dropped for the x-axis labels. When specifying a numeric value (e.g., `2`), trailing zeros are retained.
`width`	optional integer to manually adjust the width of the columns for the annotations (either a single integer or a vector of the same length as the number of annotation columns).
`xlab`	title for the x-axis. If unspecified, the function sets an appropriate axis title. Can also be a vector of three/two values (to also/only add labels at the end points of the x-axis limits).
`ilab`	optional vector, matrix, or data frame providing additional information about the studies that should be added to the plot.
`ilab.xpos`	numeric vector to specify the horizontal position(s) of the variable(s) given via `ilab`.
`ilab.pos`	integer(s) (either 1, 2, 3, or 4) to specify the alignment of the vector(s) given via `ilab` (2 means right, 4 mean left aligned). If unspecified, the default is to center the labels.
`transf`	optional argument to specify a function to transform the estimates and confidence interval bounds (e.g., `transf=exp`; see also transf). If unspecified, no transformation is used.
`atransf`	optional argument to specify a function to transform the x-axis labels and annotations (e.g., `atransf=exp`; see also transf). If unspecified, no transformation is used.
`targs`	optional arguments needed by the function specified via `transf` or `atransf`.
`rows`	optional vector to specify the rows (or more generally, the horizontal positions) for plotting the outcomes. Can also be a single value to specify the row (horizontal position) of the first outcome (the remaining outcomes are then plotted below this starting row).
`efac`	vertical expansion factor for confidence interval limits and arrows. The default value of 1 should usually work okay. Can also be a vector of two numbers, the first for CI limits, the second for arrows.
`pch`	plotting symbol to use for the estimates. By default, a filled square is used. See `points` for other options. Can also be a vector of values.
`psize`	numeric value to specify the point sizes for the estimates (the default is 1). Can also be a vector of values.
`col`	optional character string to specify the color of the estimates. Can also be a vector.
`shade`	optional character string or a (logical or numeric) vector for shading rows of the plot.
`colshade`	optional argument to specify the color for the shading.
`lty`	optional character string to specify the line type for the confidence intervals. If unspecified, the function sets this to `"solid"` by default.
`fonts`	optional character string to specify the font for the study labels, annotations, and the extra information (if specified via `ilab`). If unspecified, the default font is used.
`cex`	optional character and symbol expansion factor. If unspecified, the function sets this to a sensible value.
`cex.lab`	optional expansion factor for the x-axis title. If unspecified, the function sets this to a sensible value.
`cex.axis`	optional expansion factor for the x-axis labels. If unspecified, the function sets this to a sensible value.
`...`	other arguments.

Details

The plot shows the estimated (average) outcome with corresponding confidence interval as one study at a time is added to the analysis.

See forest.default and forest.rma for further details on the purpose of the various arguments.

Note

The function sets some sensible values for the optional arguments, but it may be necessary to adjust these in certain circumstances.

The function actually returns some information about the chosen values invisibly. Printing this information is useful as a starting point to make adjustments to the plot.

If the number of studies is quite large, the labels, annotations, and symbols may become quite small and impossible to read. Stretching the plot window vertically may then provide a more readable figure (one should call the function again after adjusting the window size, so that the label/symbol sizes can be properly adjusted). Also, the cex, cex.lab, and cex.axis arguments are then useful to adjust the symbol and text sizes.

If the outcome measure used for creating the plot is bounded (e.g., correlations are bounded between -1 and +1, proportions are bounded between 0 and 1), one can use the olim argument to enforce those limits (the observed outcomes and confidence intervals cannot exceed those bounds then).

The lty argument can also be a vector of two elements, the first for specifying the line type of the individual CIs ("solid" by default), the second for the line type of the horizontal line that is automatically added to the plot ("solid" by default; set to "blank" to remove it).

Author(s)

Wolfgang Viechtbauer wvb@metafor-project.org https://www.metafor-project.org

References

Chalmers, T. C., & Lau, J. (1993). Meta-analytic stimulus for changes in clinical trials. Statistical Methods in Medical Research, 2(2), 161–172. ⁠https://doi.org/10.1177/096228029300200204⁠

Lau, J., Schmid, C. H., & Chalmers, T. C. (1995). Cumulative meta-analysis of clinical trials builds evidence for exemplary medical care. Journal of Clinical Epidemiology, 48(1), 45–57. ⁠https://doi.org/10.1016/0895-4356(94)00106-z⁠

Lewis, S., & Clarke, M. (2001). Forest plots: Trying to see the wood and the trees. British Medical Journal, 322(7300), 1479–1480. ⁠https://doi.org/10.1136/bmj.322.7300.1479⁠

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. ⁠https://doi.org/10.18637/jss.v036.i03⁠

Examples

### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg,
              data=dat.bcg, slab=paste(author, year, sep=", "))

### fit random-effects model
res <- rma(yi, vi, data=dat)

### draw cumulative forest plots
x <- cumul(res, order=year)
forest(x, cex=0.8, header=TRUE, top=2)
forest(x, xlim=c(-4,2.5), alim=c(-2,1), cex=0.8, header=TRUE, top=2)

### meta-analysis of the (log) risk ratios using the Mantel-Haenszel method
res <- rma.mh(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg,
              slab=paste(author, year, sep=", "))

### draw cumulative forest plot
x <- cumul(res, order=year)
forest(x, xlim=c(-4,2.5), alim=c(-2,1), cex=0.8, header=TRUE, top=2)

[Package metafor version 4.6-0 Index]