contourforest (version 0.2.0)

forest_cont: Contour-enhanced Forest Plot for Continuous Outcomes

Description

Creates a forest plot for continuous outcomes (MD or SMD) with optional contour shading, prediction interval, and annotated study-level means, effect sizes, and weights.

Usage

forest_cont(
  dat,
  measure = "SMD",
  method = "REML",
  sort = c("effect", "none"),
  xlab = "",
  title = NULL,
  model = "Random-effects",
  estimator = "REML",
  m_c_col = "mean_c",
  sd_c_col = "sd_c",
  n_c_col = "n_c",
  m_t_col = "mean_t",
  sd_t_col = "sd_t",
  n_t_col = "n_t",
  diamond.col = "red",
  study.col = "blue",
  CI.col = "blue",
  Pred.Inter.col = "black",
  contour_left_min = c(-0.8, -0.5, -0.2, 0),
  contour_left_max = c(-0.5, -0.2, 0, 0.2),
  contour_right_min = c(0, 0.2, 0.5, 0.8),
  contour_right_max = c(0.2, 0.5, 0.8, 1),
  square.size = 10,
  contour_fill = c("gray95", "gray80", "gray60", "gray40"),
  text_size = 3.5,
  pred = TRUE,
  study_x = NULL,
  treatment_x = NULL,
  control_x = NULL,
  effect_x = NULL,
  weight_x = NULL,
  PredInt_x = NULL,
  xlim = NULL,
  hetero_x = -8
)

Value

A ggplot2 object representing the forest plot.

Arguments

dat

Data frame containing study-level data. Must include treatment and control means, SDs, sample sizes, and study labels.

measure

Effect size measure: "MD" (mean difference) or "SMD" (standardized mean difference). Default is "SMD".

method

Meta-analysis method for rma() (e.g., "REML"). Default is "REML".

sort

Logical. If TRUE, the studies will be sorted by effect size before plotting.

xlab

Label for the x-axis.

title

Plot title. If NULL, a default title including measure is used.

model

Model description for heterogeneity annotation. Default "Random-effects".

estimator

Estimator for heterogeneity. Default "REML".

m_c_col

Column name for control group means. Default "mean_c".

sd_c_col

Column name for control group SDs. Default "sd_c".

n_c_col

Column name for control group sample sizes. Default "n_c".

m_t_col

Column name for treatment group means. Default "mean_t".

sd_t_col

Column name for treatment group SDs. Default "sd_t".

n_t_col

Column name for treatment group sample sizes. Default "n_t".

diamond.col

Color for the pooled effect diamond. Default "red".

study.col

Color for study effect points. Default "blue".

CI.col

Color for study confidence intervals. Default "blue".

Pred.Inter.col

Color for prediction interval. Default "black".

contour_left_min

Numeric vector specifying the left-side minimum x-axis boundaries for contour shading bands (values less than the null effect).

contour_left_max

Numeric vector specifying the left-side maximum x-axis boundaries for contour shading bands (values less than or equal to the null effect).

contour_right_min

Numeric vector specifying the right-side minimum x-axis boundaries for contour shading bands (values greater than or equal to the null effect).

contour_right_max

Numeric vector specifying the right-side maximum x-axis boundaries for contour shading bands (values greater than the null effect).

square.size

Size of study points. Default 10.

contour_fill

Vector of four colors for contour shading. Default c("gray95","gray80","gray60","gray40").

text_size

Size of annotated text. Default 3.5.

pred

Logical; whether to show prediction interval. Default TRUE.

study_x

X-position for study labels. Default computed automatically.

treatment_x

X-position for treatment means. Default computed automatically.

control_x

X-position for control means. Default computed automatically.

effect_x

X-position for effect sizes. Default computed automatically.

weight_x

X-position for weights. Default computed automatically.

PredInt_x

X-position for prediction interval label. Default computed automatically.

xlim

X-axis limits. Default computed automatically.

hetero_x

X-position for heterogeneity annotation. Default -8.

Examples

forest_cont(
  dat1,
  measure = "MD",
  xlab = "Mean Difference",
  study_x = -9,
  sort = "effect",
  hetero_x = -12,
  treatment_x = -7,
  control_x = -5,
  effect_x = 5.5,
  weight_x = 10,
  PredInt_x = 7
)

forest_cont(
  dat1,
  measure = "SMD",
  xlab = "Standardized Mean Difference",
  hetero_x = -9.9,
  study_x = -7,
  sort = "effect",
  treatment_x = -5,
  control_x = -3,
  effect_x = 2.5,
  weight_x = 4,
  PredInt_x = 4
)
forest_cont(
dat1,
study.col = "darkgreen",
CI.col = "black",
diamond.col = "red",
Pred.Inter.col = "black",
measure = "SMD",
sort = "effect",
xlab = "Standardized Mean Difference",
contour_fill = c("gray90","gray70","gray50", "gray30"),
hetero_x = -9.9,
study_x = -7,
square.size = 9,
treatment_x = -5,
control_x = -3.2,
text_size = 4,
effect_x = 2.5,
weight_x = 5.8,
PredInt_x = 3
)