Generates a contour-enhanced forest plot for binary outcome data, with optional subgroup analysis. This function calculates risk ratios (or other measures) and displays study-specific effect sizes, pooled subgroup effects, overall pooled effect, and heterogeneity statistics (I^2, tau^2).
forest_bin_subgroup(
dat,
subgroup = NULL,
measure = "RR",
method = "REML",
nc_col = "n_c",
ne_col = "n_t",
event_c_col = "events_c",
event_t_col = "events_t",
xlab = "Risk Ratio (RR)",
title = "Subgroup Forest Plot",
diamond.col = "red",
overall.col = "darkgreen",
study.col = "blue",
CI.col = "blue",
Pred.Inter.col = "black",
square.size = 8,
Pred.Int.size = 2,
xlim = c(-2, 3.5),
tlim = c(0, 2.3),
text_size = 3.5,
xpos = list(EventsT = -1, EventsC = -0.4, Effect = 2.5, Weight = 3.2),
study_x = -1.8,
val_x = 2.6,
contour_left_min = c(0, 0.5, 0.67, 0.83),
contour_left_max = c(0.5, 0.67, 0.83, 1),
contour_right_min = c(1, 1.2, 1.5, 2),
contour_right_max = c(1.2, 1.5, 2, 2.5),
pred = TRUE
)A ggplot object representing the contour-enhanced subgroup forest plot.
A data frame containing study-level data. Must include columns for treatment and control events and sample sizes, and optionally a subgroup column.
Column name in dat specifying subgroup membership (character or factor). Defaults to NULL (no subgroups).
Effect measure to use. Default is "RR" (risk ratio).
Method for random-effects meta-analysis. Default is "REML".
Character. Column name for control group sample sizes.
Character. Column name for treatment group sample sizes.
Character. Column name for number of events in control group.
Character. Column name for number of events in treatment group.
Label for the x-axis. Default is "Risk Ratio (RR)".
Plot title. Default is "Subgroup Forest Plot".
Color for subgroup pooled effect diamonds. Default is "red".
Color for overall pooled effect diamond. Default is "darkgreen".
Color for individual study points. Default is "blue".
Color for study confidence interval bars. Default is "blue".
Color for prediction interval bars. Default is "black".
Maximum size of study points. Default is 8.
Thickness of prediction interval line. Default is 2.
Numeric vector of length 2 giving x-axis limits. Default is c(-2, 3.5).
Numeric vector of length 2 for truncating study CIs. Default is c(0, 2.3).
Base text size for labels. Default is 3.5.
List of x-axis positions for EventsT, EventsC, Effect, and Weight labels. Default is list(EventsT=-1, EventsC=-0.4, Effect=2.5, Weight=3.2).
X position for study names. Default is -1.8.
X position for pooled effect labels. Default is 2.6.
Numeric vector defining left contour minimum values. Default is c(0, 0.5, 0.67, 0.83).
Numeric vector defining left contour maximum values. Default is c(0.5, 0.67, 0.83, 1).
Numeric vector defining right contour minimum values. Default is c(1, 1.2, 1.5, 2).
Numeric vector defining right contour maximum values. Default is c(1.2, 1.5, 2, 2.5).
Logical indicating whether to show the prediction interval. Default is TRUE.
# Load example dataset
data <- bcg()
# Generate subgroup forest plot
forest_bin_subgroup(
dat = bcg(),
tlim = c(0, 2.3),
contour_left_min = c(0, 0.3, 0.5, 0.7),
contour_left_max = c(0.3, 0.5, 0.7, 1),
contour_right_min = c(1, 1.2, 1.5, 1.8),
contour_right_max = c(1.2, 1.5, 1.8, 2.4)
)
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