Tofameta

• Tofameta
  • Introduction
    • R scripts
    • Data
  • Running the analysis
    • Conventional approach
    • Docker image
  • Metadata
    • Efficacy
    • Safety

Introduction

R scripts/data for Lucaciu & Constantine-Cooke et al. Real-world experience with tofacitinib in ulcerative colitis - a systematic review and meta-analysis of observational studies. The scripts have been written by Nathan Constantine-Cooke and are licensed under the MIT license.

R scripts

R scripts can be found in the R/ directory.

analysis.R is the primary R script for performing the analysis.

functions.R contains the MetaAnalysis function used to perform the analysis. This file does not need to be loaded separately, as this function will be loaded when analysis.R is run.

Data

The data used for this analysis can be found in the data/ directory.

efficacy.csv and safety.csv contain the data used for the pooled analysis of efficacy and safety respectively. Metadata for these files can be found below in the dedicated section of this file.

Running the analysis

Assuming R is already installed, this analysis can be run either conventionally (using the Rscript terminal command or Rstudio IDE), or via a Docker container. The former is easier to use and more conventional to those familiar to R, whilst the latter is optimal for reproducibility. Docker uses an image of the environment used to produce the analysis and will , in theory, reproduce the same output many years into the future regardless of changes to operating systems or the R programming language.

Conventional approach

If taking the conventional approach, the analysis can be run in just three lines from a bash/zsh terminal.

git clone https://github.com/nathansam/tofameta
cd tofameta
Rscript R/analysis.R

You can now find the generated plots and csv files in the output/ folder within the tofameta/ directory.

For those adverse to using git, you may prefer to download the source code from the releases page, unzip the source code and then run the analysis.R script in the R/ folder. Please note that your working directory will need to be the top-level directory of tofameta.

Docker image

If Docker is not already installed, then first install Docker. Running the below script in a bash/zsh terminal will pull the tofameta image from GitHub's servers, rename the image to a much nicer name than the format GitHub insists on, and run the analysis. Note that in order to save the output outside of the container, a volume must be specified using -v. Simply replace <OUTPUTdirectory> in the below script with a path to where you wish the output to be saved.

# Pull image
docker pull ghcr.io/nathansam/tofameta/tofameta:v1.0
# Rename to a nicer tag
docker tag ghcr.io/nathansam/tofameta/tofameta:v1.0 nathansam/tofameta
# Delete the old tag
docker rmi ghcr.io/nathansam/tofameta/tofameta:v1.0
# Run the docker image
docker run -v <OutputDirectory>:/analysis/output nathansam/tofameta

Replacing <OutputDirectory> with the path to a directory you wish to store the output from the script in (such as ~/output).

If you wish to delete the image (around 1.09GB in size) after the analysis has finished then run

docker rmi -f nathansam/tofameta

Metadata

This section provides descriptions of each of the columns in each data file.

Efficacy

• Year: Year study was published
• Author: First author of study
• ni: Participants in the study
• UC: Participants with ulcerative colitis.
• earlyResp: Clinical response during the induction phase
• lateResp: Clinical response during the maintenance phase
• earlyRem: Clinical remission during the induction phase
• lateRem: Clinical remission during the maintenance phase
• earlySFrem: Steroid free clinical remission during the induction phase
• lateSFrem24: Steroid free clinical remission during the maintenance phase
• endorem: Endoscopic remission at any time point
• CD: Participants with Crohn's disease
• IBDU: Participants with inflammatory bowel disease unclassified
• earlyTot: Participants in study during induction phase
• lateTot: Participants in study during maintenance phase
• antiTNF: AntiTNF medication prior to joining study.
• vedo: Vedolizumab medication prior to joining study
• uste: Ustekinumab medication prior to joining study
• noBio: No biological medication prior to joining study (bio naive)
• endoTot: Underwent endoscopy
• age: Median age of patients in the study

Safety

• Year: Year study was published
• Author: First author of study
• ni: Participants in the study
• Followup: Median followup for the study
• AE: Experienced an adverse event
• HZ: Developed herpes zoster
• Dyslipidemia: Developed dyslipidemia
• malignancy: Developed malignancy
• Colectomy: Underwent colectomy
• PNR Primary non-response
• LOR Loss of response
• InfectionM: Mild/moderate infection
• InfectionS: Severe infection
• discont: Treatment discontinuation at any time point
• discontAE: Treatment discontinued due to adverse event
• discontColectomy: Treatment discontinued due to colectomy
• discontPNR: Treatment discontinued due to primary non-response
• discontLOR: Treatment discontinued due to loss of response
• discontPatient: Treatment discontinued due to primary non-response