metaviz (version 0.3.1)

viz_rainforest: Rainforest plots for meta-analyses

Description

Creates a rainforest plot, a novel variant of the forest plot.

Usage

viz_rainforest(
  x,
  group = NULL,
  type = "standard",
  method = "FE",
  study_labels = NULL,
  summary_label = NULL,
  confidence_level = 0.95,
  detail_level = 1,
  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,
  ...
)
rainforest(
  x,
  group = NULL,
  type = "standard",
  method = "FE",
  study_labels = NULL,
  summary_label = NULL,
  confidence_level = 0.95,
  detail_level = 1,
  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'.

method

character string indicating which method should be used to compute the study weights and summary effect(s). Can be any method argument from rma.uni (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 and likelihood raindrops.

detail_level

numeric value. Values larger than 1 lead to a higher plotting detail (i.e., smoother likelihood raindrop polygons and more fluent color shading), values smaller than 1 to less plotting detail compared to the default plot.

col

character string specifying the color palette for plotting study-level results from package RColorBrewer. Can be any of "Blues", "Greys", "Oranges", "Greens", "Reds", and "Purples".

summary_col

character string specifying the color for plotting the summary effect(s). Can be any of "Blues", "Greys", "Oranges", "Greens", "Reds", and "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, 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.

...

deprecated argument names from earlier versions can still be passed to viz_rainforest via ....

Value

A Rainforest plot is created using ggplot2.

Details

Rainforest plots were proposed by Schild and Voracek (2015) as a variant and enhancement of classic forest plots. Rainforest plots use (log-)likelihood drops to depict study level results (for details, see Barrowman & Myers, 2003). viz_rainforest assumes normality of effect sizes to construct these (log-)likelihood drops. The width of each such raindrop is identical to the width of the confidence interval. For a given (log-)likelihood raindrop, the height can be interpreted as the plausibility (i.e., (log-)likelihood value) for different true values given the observed estimate. Moreover, the height of each raindrop is scaled with respect to its relative meta-analytic weight considering all studies. Therefore, visually comparing heights of different raindrops highlights the relative importance within the meta-analysis. In addition, color shading is utilized to further visualize statistical uncertainty, as suggested by Jackson (2008). Finally, study and summary level point estimates are depicted clearly by a specific symbol.

Rainforest plots have the following advantages, as compared to classic forest plots:

1. The width of the likelihood raindrops corresponds to the confidence intervals, as also shown in the classic forest plot. In addition, for each likelihood drop the height (and color shading) visualizes the plausibility of true values given the observed estimate.

2. Low likelihood drops and light color shading causes small studies (with wide confidence intervals and less weight in the meta-analysis) to be visually less dominant.

3. In classic forest plots, it is often hard to depict the magnitude of point estimates to a reasonable degree of accuracy, especially for studies with large meta-analytic weights and correspondingly large plotting symbols (commonly squares). Specific symbols within the raindrops improve the visualization of study point estimates.

Note that for subgroup analysis the height of each raindrop is scaled by the weight of each study within the subgroup divided by the total weight sum of all studies irrespective of subgroup. Therefore, with subgroups present, the overall impression of raindrop heights and color shading within a given subgroup compared to other subgroups conveys information about the relative precision of the meta-analytic subgroup estimates.

References

Barrowman, N. J., & Myers, R. A. (2003). Raindrop plots: A new way to display collections of likelihoods and distributions. American Statistician, 57, 268-274.

Jackson, C. H. (2008). Displaying uncertainty with shading. American Statistician, 62, 340-347.

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 a rainforest plot using the mozart data
viz_rainforest(x = mozart[, c("d", "se")],
study_labels = mozart[, "study_name"], xlab = "Cohen d")

# Visualizing a subgroup analysis of published and unpublished studies
viz_rainforest(x = mozart[, c("d", "se")], group = mozart[, "rr_lab"],
study_labels = mozart[, "study_name"], method = "REML",
summary_label = c("Summary (rr_lab = no)", "Summary (rr_lab = yes)"),
xlab = "Cohen d")

# Showing additional information in aligned tables. Log risk ratios are labeled
# in their original metric (risk ratios) on the x axis.
viz_rainforest(x = exrehab[, c("logrr", "logrr_se")],
annotate_CI = TRUE, xlab = "RR", x_trans_function = exp,
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 = "")))
# }