contourforest

Contour-Enhanced Forest Plots for Meta-Analysis

This R package provides functions to create contour-enhanced forest plots for meta-analysis of continuous and binary outcomes. It integrates with metafor and ggplot2 and allows customization of colors, labels, prediction intervals, and subgroup analyses.

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contourforest

A detailed guide for using the contourforest package.

📖 Read the Full Guide

For step-by-step instructions and examples, visit the RPubs guide:
https://rpubs.com/umarhussain/1382129

🚀 How to Run on Different Types of Datasets

The guide covers running contourforest on various dataset formats and scenarios. Follow the instructions in the RPubs tutorial to get started quickly.

Installation

# Install remotes if not already installed
# install.packages('remotes')

remotes::install_github('umarhussain-git/contourforest')

Functions

bcg - BCG Vaccine Trials Dataset dat1- Hypothetical dataset: GAD-7 Anxiety Scores by Socioeconomic Status forest_bin() - Contour-Enhanced Binary Outcome Forest Plot forest_bin_subgroup() - Subgroup Forest Plot for Binary Outcome Meta-analysis forest_cont() - Contour-enhanced Forest Plot for Continuous Outcomes forest_cont_subgroup() - Forest Plot for Subgroup Meta-Analysis

Example Datasets

dat – Hypothetical dataset: Depression by Socioeconomic Status

dat1 – Hypothetical dataset: GAD-7 Anxiety Scores by Socioeconomic Status library(contourForest)

Example 1: Binary Outcomes

#` Load example dataset

data(bcg())

#' Generate a binary forest plot (Odds Ratio)

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)
)

library(contourforest)

#' Load example dataset

data(dat1)

#' View first rows

head(dat1)

Example 2: Continuous Outcomes

#' Generate a continuous forest plot (Standardized Mean Difference or Mean Difference)

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
)

Example 3: Binary Outcomes with Subgroups

library(contourForest)

#, Generate a binary forest plot with subgroups

forest_bin_subgroup(bcg(), measure = 'OR')

Example 4: Continuous Outcomes with Subgroups

library(contourforest)

#` Generate a continuous forest plot with subgroups

# Using the built-in dataset dat1
forest_cont_subgroup(dat1)

Features

Contour-enhanced visualization to highlight statistical significance

Supports Odds Ratios (OR), Risk Ratios (RR), Mean Differences (MD), and Standardized Mean Differences (SMD)

Optional prediction intervals

Customizable colors, labels, and annotation positions

Subgroup analysis with pooled effects

forest_cont() – Forest plot for continuous outcomes

forest_cont_subgroup() – Forest plot for continuous outcomes with subgroups

Maintainer

Umar Hussain

drumarhussain@gmail.com

Assistant professor, Orthodontics

Saidu college of Dentistry