Nov 28, 2023
An Open-Source System for Efficient Clinical Trial Support: the COMET study experience
Peer-reviewed method
- jclutton 1,
- evidoni 1,
- Dinesh Pal Mudaranthakam1,
- Robert Neal Montgomery1,
- Erin Blocker2,
- Ashley Shaw1,
- Amanda Szabo Reed1
- 1University of Kansas Medical Center;
- 2Emporia State University
- PLOS ONE Lab ProtocolsTech. support email: [email protected]
External link: https://doi.org/10.1371/journal.pone.0293874
Protocol Citation: jclutton , evidoni , Dinesh Pal Mudaranthakam, Robert Neal Montgomery, Erin Blocker, Ashley Shaw, Amanda Szabo Reed 2023. An Open-Source System for Efficient Clinical Trial Support: the COMET study experience. protocols.io https://dx.doi.org/10.17504/protocols.io.rm7vzxo25gx1/v1
Manuscript citation:
Clutton J, Montgomery RN, Mudaranthakam DP, Blocker EM, Shaw AR, et al. (2023) An open-source system for efficient clinical trial support: The COMET study experience. PLOS ONE 18(11): e0293874. https://doi.org/10.1371/journal.pone.0293874
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: November 01, 2023
Last Modified: November 28, 2023
Protocol Integer ID: 90202
Keywords: comet, r, fitbit, clinical trial support, data infrastructure, informatics, comet study experience exercise clinical trial, source system for efficient clinical trial support, efficient clinical trial support, comet study experience exercise, clinical trial, automated study system, other study team, adaptation by other study team, comet approach to data management, study system, combined exercise trial, study team, data management, rigorous execution of comet, study, comet approach, study stakeholder, target enrollment
Funders Acknowledgements:
National Institute on Aging
Grant ID: P30 AG072973
National Institute on Aging
Grant ID: RO1 AG070036
National Institute on Aging
Grant ID: K01 AG072034
Leo and Anne Albert Charitable Trust
Grant ID: N/A
Disclaimer
The authors make no guarantee of accuracy or compatibility with the user's system or project.
Abstract
Exercise clinical trials are complex, logistically burdensome, and require a well-coordinated multi-disciplinary approach. Challenges include managing, curating, and reporting on many disparate information sources, while remaining responsive to a variety of stakeholders. The Combined Exercise Trial (COMET, NCT04848038) is a one-year comparison of three exercise modalities delivered in the community. Target enrollment is 280 individuals over 4 years. To support rigorous execution of COMET, the study team has developed a suite of scripts and dashboards to assist study stakeholders in each of their various functions. The result is a highly automated study system that preserves rigor, increases communication, and reduces staff burden. This manuscript describes system considerations and the COMET approach to data management and use, with a goal of encouraging further development and adaptation by other study teams in various fields.
Guidelines
This data infrastructure project was specially designed for the Combined Exercise Trial (https://doi.org/10.1016/j.cct.2022.106805); however, with some adaptations it can be applied to other studies. We suggest using the data flow diagram below and the paper to plan out what aspects of the data infrastructure you'd like to use.
Figure 1: The COMET daily data infrastructure.
Safety warnings
The code will not work automatically and will require a heavy amount of adaptation. The COMET study is underway until at least 2026; therefore the data and more importantly, the structure of the data are private. This will require some interpretation of the user to adapt. We hope to make the data public when possible.
Ethics statement
This work was funded by grants from the National Institute on Aging P30 AG072973, R01 AG070036, K01 AG072034, and the Leo and Anne Albert Charitable Trust which do not necessarily endorse the statements. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Before start
Software
R programming language
NAME
The R Foundation
DEVELOPER
REPOSITORY
SOURCE LINK
Software
R Studio Desktop
NAME
The R Studio, Inc.
DEVELOPER
SOURCE LINK
Planning
Review the code (GitHub - cometstudy/OSSforEfficientClinicalTrialSupportCOMET) and plan what aspects of the project you'd like to adapt.
Some possibilities include:
- Fitbit data infrastructure
- DSMC reporting
- Study staff reporting
- Email modules
- Data storage
- Some approximation of the whole project
The code is designed to work in combination with a REDCap project. The data dictionary for the REDCap project can be found here: cometstudy/OSSforEfficientClinicalTrialSupportCOMET (github.com). We suggest getting a feel for the REDCap project before making decisions about what aspects of the code you'd like to adapt.
Note: Some surveys have been removed from the REDCap project, as they are available in the REDCap Instrument Library.
Operationalize
If you plan to use parts of the project that require REDCap instruments, i.e. output scripts:
- Download the data dictionary from the GitHub project: cometstudy/OSSforEfficientClinicalTrialSupportCOMET (github.com)
- Upload the data dictionary into your REDCap project.
If you plan to use parts of the project that require REDCap, but don't have access to REDCap, you may be able to approximate a similar system using other Electronic Data Capture or data storages systems.
Download the code: GitHub - cometstudy/OSSforEfficientClinicalTrialSupportCOMET
The project infrastructure runs every morning using the comet_nucleus.R script. This script can be used to get acquainted with the daily processes. The script may also be set to run in an automated fashion using a cronjob or similar system operation.
All names, emails, drives, and pathways have been scrubbed from the code. Be sure to correct them. A file with all scrubbed pathways is included.
Note: To find every instance of a text in a directory (i.e. a scrubbed pathway), you can use the ctrl+shift+f command in Rstudio.
scrubbed_pathways.xlsx8KB Adapt the project to fit your needs!