metaviz (version 0.3.1)

viz_forest: Forest plot variants for meta-analyses

Description

Creates a rainforest, thick forest, or classic forest plot.

Usage

viz_forest(
  x,
  group = NULL,
  type = "standard",
  variant = "classic",
  method = "FE",
  study_labels = NULL,
  summary_label = NULL,
  confidence_level = 0.95,
  col = "Blues",
  summary_col = col,
  text_size = 3,
  xlab = "Effect",
  x_limit = NULL,
  x_trans_function = NULL,
  x_breaks = NULL,
  annotate_CI = FALSE,
  study_table = NULL,
  summary_table = NULL,
  table_headers = NULL,
  table_layout = NULL,
  ...
)

Arguments

x

data.frame or matrix with the effect sizes of all studies (e.g., correlations, log odds ratios, or Cohen d) in the first column and their respective standard errors in the second column. Alternatively, x can be the output object of function rma.uni from package metafor; then effect sizes and standard errors are extracted from x.

group

factor indicating the subgroup of each study to plot a subgroup forest plot. Has to be in the same order than x.

type

character string indicating the type of forest plot to be plotted. Can be "standard" (default), "study_only", "summary_only", "cumulative", or "sensitivity". See 'Details'.

variant

character string indicating the forest plot variant that should be plotted. Can be "classic" (default) for a traditional forest plot, "rain" for rainforest plot, "thick" for a thick forest plot.

method

character string indicating which method should be used to compute the study weights and summary effect(s). Can be any method argument from function rma.uni of package metafor (e.g., "FE" for the fixed effect model, or "DL" for the random effects model using the DerSimonian-Laird method to estimate \(\tau^2\)). If input x is an output object of function rma.uni from package metafor, then the method is extracted from x.

study_labels

a character vector with names/identifiers to annotate each study in the forest plot. Has to be in the same order than x. Ignored if study_table and/or summary_table is supplied.

summary_label

a character string specifying the name to annotate the summary effect. If a subgroup analysis is plotted, summary_label should be a character vector with a name for each subgroup summary effect, arranged in the order of the levels of group. Ignored if study_table and/or summary_table is supplied.

confidence_level

numeric value. The confidence level for the plotted confidence intervals.

col

character string specifying the main color for plotting study-level results. For variant = "thick" and variant = "classic" can be a vector of length nrow(x) with colors for each study-level result individually. For variant = "rain" must be one of the following palettes from package RColorBrewer: "Blues", "Greys", "Oranges", "Greens", "Reds", or "Purples".

summary_col

character string specifying the main color for plotting the summary effect(s). For variant = "thick" and variant = "classic" can be a vector with colors for each subgroup summary effect individually. For variant = "rain" with type = "summary_only" must be one of the following palettes from package RColorBrewer: "Blues", "Greys", "Oranges", "Greens", "Reds", or "Purples".

text_size

numeric value. Size of text in the forest plot. Default is 3.

xlab

character string specifying the label of the x axis. By default also used for the header of the aligned table if annotate_CI is TRUE.

x_limit

numeric vector of length 2 with the limits (minimum, maximum) of the x axis.

x_trans_function

function to transform the labels of the x axis. Common uses are to transform log-odds-ratios or log-risk-ratios with exp to their original scale (odds ratios and risk ratios), or Fisher's z values back to correlation coefficients using tanh.

x_breaks

numeric vector of values for the breaks on the x-axis. When used in tandem with x_trans_function the supplied values should be not yet transformed.

annotate_CI

logical scalar. Should the effect size and confidence interval values be shown as text in an aligned table on the right-hand side of the forest plot?

study_table

a data.frame with additional study-level variables which should be shown in an aligned table. Has to be in the same order than x.

summary_table

a data.frame with additional summary-level information shown in an aligned table. If group is supplied, summary_table must have a row for each subgroup summary effect, arranged in the order of the levels of group.

table_headers

character vector. Headers for each column of aligned tables via study_table, summary_table, and/or annotate_CI.

table_layout

numeric layout matrix passed to layout_matrx of arrangeGrob. Can be used to overwrite the default spacing of the forest plot and aligned tables via study_table, summary_table, and annotate_CI.

...

further arguments passed to viz_rainforest for variant = "rain", or viz_thickforest for variant = "thick".

Value

A forest plot is created using ggplot2.

Details

The forest plot is the most widely used display to visualize meta-analytic results. The function viz_forest creates visually appealing and informative-rich forest plots using ggplot2. Many options to flexibly customize the visual appearance and statistical information displayed are provided. In addition, rainforest plots as well as the thick forest plots can be created, two variants and enhancements of the classical forest plot recently proposed by Schild and Voracek (2015). For further details see the documentation of the wrapper functions viz_rainforest, and viz_thickforest.

Available forest plot types

Different aspects of meta-analytic data can be shown in forest plots. Five different types are available in viz_forest via the type parameter. Argument "standard" (default) shows study results as well as summary results in the forest plot. "study_only" allows to only show study results without the meta-analytic summary estimate. "summary_only" can be used to only show meta-analytic summary estimate(s), which is primarily useful to visualize several subgroup results (using group). "cumulative" shows a cumulative meta-analysis, that is, meta-analytic summary effects are computed sequentially by adding each study one-by-one. Studies are added in the same order than they were supplied in x. Finally, "sensitivity" shows for each study the meta-analytic summary effect if that particular study is not considered in the computation of the summary effect (leave-one-out analysis).

References

Schild, A. H., & Voracek, M. (2015). Finding your way out of the forest without a trail of bread crumbs: Development and evaluation of two novel displays of forest plots. Research Synthesis Methods, 6, 74-86.

Examples

# NOT RUN {
library(metaviz)
# Plotting the mozart data using a classic forest plot
viz_forest(x = mozart[, c("d", "se")],
study_labels = mozart[, "study_name"], xlab = "Cohen d")

# Subgroup analysis of published and unpublished studies shown in a rainforest plot
viz_forest(x = mozart[, c("d", "se")], study_labels = mozart[, "study_name"], method = "REML",
variant = "rain", summary_label = c("Summary (rr_lab = no)", "Summary (rr_lab = yes)"),
group = mozart[, "rr_lab"], xlab = "Cohen d")

# Thick forest plot with additional information in aligned tables. Log risk
# ratios are labeled in their original metric (risk ratios) on the x axis.
viz_forest(x = exrehab[, c("logrr", "logrr_se")], variant = "thick",
xlab = "RR", x_trans_function = exp, annotate_CI = TRUE,
study_table = data.frame(
Name = exrehab[, "study_name"],
eventsT = paste(exrehab$ai, "/", exrehab$ai + exrehab$bi, sep = ""),
eventsC = paste(exrehab$ci, "/", exrehab$ci + exrehab$di, sep = "")),
summary_table = data.frame(
Name = "Summary",
eventsT = paste(sum(exrehab$ai), "/", sum(exrehab$ai + exrehab$bi), sep = ""),
eventsC = paste(sum(exrehab$ci), "/", sum(exrehab$ci + exrehab$di), sep = "")),
table_layout = matrix(c(1, 1, 2, 2, 3), nrow = 1))
# }