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Network Meta‑Analysis (NMA) of Continuous Outcomes

This repository provides a complete, reproducible R script for random‑effects network meta‑analysis of continuous outcome data using the standardised mean difference (SMD). The script performs all essential steps: data preparation, network geometry, model fitting, inconsistency assessment, treatment ranking, and publication bias evaluation. All results are saved as CSV files and high‑resolution PNG figures.

📋 Table of Contents

✨ Features

  • Converts arm‑based data to pairwise (contrast‑based) format automatically
  • Draws a network geometry plot with treatment nodes and direct comparisons
  • Fits a random‑effects NMA (frequentist, using netmeta)
  • Assesses global heterogeneity (τ², I², Cochran’s Q)
  • Evaluates global inconsistency (design‑by‑treatment decomposition) and local inconsistency (node‑splitting)
  • Computes P‑scores (frequentist analogue of SUCRA) for treatment ranking
  • Generates a comparison‑adjusted funnel plot for publication bias detection
  • Saves all numerical results (CSV) and figures (PNG, 800 DPI)

📥 Input Data Requirements

Prepare an Excel file named nma_input.xlsx with the following columns (exact names required):

Column name Description Example
Study Study identifier (character or numeric) "Smith 2020"
Treatment Treatment name/identifier "A", "Placebo"
Mean Mean outcome value in that arm 2.5
SD Standard deviation of the outcome 1.2
N Number of participants in that arm 30

Important notes:

  • Each study must contribute at least two rows (one per arm). Multi‑arm studies are fully supported.
  • Treatment names must be consistent across studies (e.g., do not write "A" in one study and "A (low dose)" in another).
  • The script assumes a continuous outcome and uses SMD as the effect measure. For binary outcomes or other effect sizes, modify the sm argument (see 🔧 Customisation Guide).

Example data snippet:

Study Treatment Mean SD N
Smith 2020 A 2.5 1.2 30
Smith 2020 B 3.1 1.5 32
Jones 2021 A 2.8 1.3 28
Jones 2021 C 3.5 1.4 29

Place this file in the same directory as the R script before running.

📤 Output Files

After successful execution, the working directory will contain the following files:

File name Description
01_pairwise_data.csv Pairwise (contrast‑based) data used for NMA
01_network_plot.png Network geometry plot (nodes = treatments, edges = direct comparisons)
02_nma_results.csv Random‑effects NMA results – all treatment comparisons vs. every treatment
02_forest_plot.png Forest plot comparing all treatments against a reference (default: "C-ESPB")
03_heterogeneity.csv Global heterogeneity statistics (Q, p‑value, I², τ, τ²)
04_global_inconsistency.csv Design‑by‑treatment decomposition (consistency vs. inconsistency)
05_local_inconsistency.png Node‑splitting forest plot (direct vs. indirect evidence for each contrast)
06_treatment_ranking.csv P‑scores for each treatment (higher = better outcome)
06_ranking_plot.png Bar plot of P‑scores
07_funnel_plot.png Comparison‑adjusted funnel plot (ordered by P‑score)

🚀 How to Run

1. Install required R packages

Open R or RStudio and run:

install.packages(c("readxl", "dplyr", "netmeta", "ggplot2", "meta"))

🔧 Customisation Guide

What you want to change Where to modify in the script
Reference treatment for forest plot In forest(net, ref = "C-ESPB", ...) – replace "C-ESPB" with a valid treatment name from your data
Effect measure (e.g., MD, OR, RR) In pairwise(..., sm = "SMD") and netmeta(..., sm = "SMD") – change "SMD" to "MD", "OR", etc.
Fixed-effect instead of random-effects In netmeta() set common = TRUE, random = FALSE
Plot resolution / size Modify width, height, res in each png() call (e.g., width = 10, height = 8, res = 600)
Order of treatments in funnel plot Change the order argument inside funnel() – by default it uses the P-score ranking
Title or axis labels Add or modify main, xlab, ylab in the respective plotting calls

📜 Citation & License

If you use this script in a publication, please cite the core R packages:

Rücker G, Schwarzer G, Krahn U, König J (2024). netmeta: Network Meta‑Analysis using Frequentist Methods. R package version 2.8-0.
Schwarzer G, Carpenter JR, Rücker G (2015). meta: An R package for meta‑analysis. R News, 15(3): 9–13.

License: MIT

Disclaimer: This script is provided “as is”, without warranty of any kind. The authors are not responsible for any errors or consequences arising from its use.