The goal is to explore the reproducibility of independent analyses conducted by three teams, consisting of the PhD students from the SHARE-CTD project. Each team has a clinical and a statistical expert assigned. The task for every team is to conduct an IPDMA assessing the efficacy of esketamine in reducing suicidality among patients with major depressive disorder. The SHARE-CTD datathon provides a unique opportunity to investigate this task in a multi-team analysis. Access to the clinical trial data was granted via the YODA project, for which a master protocol was submitted to YODA.
This repository contains the R code and analysis output for Team 1's individual participant data meta-analysis (IPDMA) of outcomes from randomized controlled trials of esketamine. The registered SAP for team 1 is openly available on OSF.
Team 1 conducted two analyses:
Primary outcome: adverse events related to suicidal behaviour.
Secondary outcome: suicidal ideation measured using the Columbia Suicide Severity Rating Scale (C-SSRS).
The analysis has been performed with R version 4.3.0.
The primary outcome analysis uses the following packages:
- library(haven)
- library(lubridate)
- library(tidyr)
- library(tidyverse)
- library(dplyr)
- library(meta)
- library(metafor)
- library(lme4)
- library(logistf)
- library(ggplot2)
- library(survival)
- library(survminer)
The secondary outcome analysis uses:
- library(haven)
- library(dplyr)
- library(tidyr)
- library(purrr)
- library(stringr)
- library(ggplot2)
- library(ordinal)
- library(lmerTest)
The two analyses are independent and do not need to be run sequentially. If reproducing the complete Team 1 analysis, the recommended order is:
- PrimaryOutcome_Team1.txt
- SecondaryOutcome_Team1.html
However, this is an organizational order rather than a computational dependency. The secondary analysis imports and prepares its own data and does not require objects created by the primary outcome script.
The analyses depend on access to the original individual participant data. These data are not included in this repository. Consequently, the code can only be fully reproduced by researchers who have authorized access to the underlying trial datasets.
Cora Burgwinkel, Mian Haider Ali, Tobechi Obinwanne, Ka Hin Tai, Ulrike Held, Florian Naudet
This work was funded by SHARE-CTD. SHARE-CTD is funded by the European Union under the grant agreement Horizon-MSCA.2022-DN~101120360. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. SHARE-CTD (Sharing and Re-using Clinical Trial Data to maximise impact) is a doctoral network funded by the European Union that involves 11 principal investigators and 16 academic and non-academic partners. This project was motivated by the growing movement toward open science and by the need to define good practices for the preparation and use of clinical trial data. Its aim is to train a new generation of biomedical researchers with a deep understanding of the processes, values, and benefits of clinical trial data sharing. To gain a comprehensive understanding, biomedical researchers must be trained in areas such as data science, trial regulation, meta-research, as well as ethical, legal, and social issues.