TITLE: An Open-Source System for Efficient Clinical Trial Support: the COMET study experience
AUTHORS: jclutton, evidoni, Dinesh Pal Mudaranthakam, Robert Neal Montgomery, Erin Blocker, Ashley Shaw, Amanda Szabo Reed

[DESCRIPTION]
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.

[BEFORE_START]
1. Be sure you have R downloaded (R: The R Project for Statistical Computing (r-project.org))
 

2. Be sure you have python downloaded (Download Python | Python.org)
 

3. We recommend using Rstudio  as an R environment (Download RStudio - Posit)

 




[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.  
 

[STEPS]
SECTION: Planning
1. 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
SECTION: Operationalize
3. 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.
SECTION: Operationalize
4. Download the code: GitHub - cometstudy/OSSforEfficientClinicalTrialSupportCOMET

SECTION: Operationalize
5. 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.


SECTION: Operationalize
6. 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.

   

2. 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.
SECTION: Operationalize
7. Adapt the project to fit your needs!