contourforest (version 0.2.0)

forest_bin: Contour-Enhanced Binary Outcome Forest Plot

Description

Generates a contour-enhanced forest plot for binary outcome data (e.g., odds ratios, risk ratios), with study-level effects, confidence intervals, pooled effect, prediction interval, and heterogeneity statistics.

Usage

forest_bin(
  dat,
  measure = "OR",
  method = "REML",
  xlab = "",
  title = NULL,
  model = "Random-effects",
  estimator = "REML",
  nc_col = "n_c",
  ne_col = "n_t",
  event_c_col = "events_c",
  event_t_col = "events_t",
  diamond.col = "red",
  study.col = "blue",
  CI.col = "blue",
  Pred.Inter.col = "black",
  square.size = 10,
  contour_fill = c("gray95", "gray80", "gray60", "gray40"),
  text_size = 3.5,
  xlim = c(-1.7, 3.5),
  pred = TRUE,
  xpos = list(EventsT = -0.9, EventsC = -0.3, Effect = 2.6, Weight = 3.1),
  study_x = -1.8,
  hetero_x = -1.7,
  tlim = c(0, 2.3),
  truncate_PI = FALSE,
  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)
)

Value

A ggplot2 object of the forest plot.

Arguments

dat

Data frame containing study-level binary outcome data.

measure

Character. Effect measure ("OR" for odds ratio, "RR" for risk ratio, etc.).

method

Character. Method for meta-analysis heterogeneity estimation (default "REML").

xlab

Character. Label for the x-axis.

title

Character. Plot title. If NULL, a default title is generated.

model

Character. Meta-analysis model ("Random-effects" or "Fixed-effects").

estimator

Character. Estimator used in the meta-analysis (default "REML").

nc_col

Character. Column name for control group sample sizes.

ne_col

Character. Column name for treatment group sample sizes.

event_c_col

Character. Column name for number of events in control group.

event_t_col

Character. Column name for number of events in treatment group.

diamond.col

Color of the pooled effect polygon.

study.col

Color of the study-level effect points.

CI.col

Color of the study-level confidence interval lines.

Pred.Inter.col

Color of the prediction interval line.

square.size

Numeric. Maximum size of study-level effect squares.

contour_fill

Vector of colors for contour shading levels.

text_size

Numeric. Base size of plot text.

xlim

Numeric vector of length 2. Limits of the x-axis.

pred

Logical. Whether to show the prediction interval.

xpos

List of numeric positions for text labels (EventsT, EventsC, Effect, Weight).

study_x

Numeric. X-position for study names.

hetero_x

Numeric. X-position for heterogeneity text.

tlim

Numeric vector of length 2. Limits for truncating study confidence intervals.

truncate_PI

Logical. Whether to truncate the prediction interval to tlim.

contour_left_min

Numeric vector. Minimum x-values for left-side contour shading.

contour_left_max

Numeric vector. Maximum x-values for left-side contour shading.

contour_right_min

Numeric vector. Minimum x-values for right-side contour shading.

contour_right_max

Numeric vector. Maximum x-values for right-side contour shading.

Examples

forest_bin(
  dat = bcg(),
  measure = "OR",
  xlab = "Odds Ratio",
  title = "BCG Vaccine Meta-analysis",
  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(1.2,1.5,1.8,2.5)
)