Open Source Technologies in Clinical Research
Open Source Technologies in Clinical Research aims to provide guidance to the use of open source technologies in regulatory environments within the pharmaceutical industry, including but not limited to R and Python. Our intent is to be a repository of knowledge for:
• Use Cases
• Implementation and validation guidance
• Best Practices
Our goal is to broaden the acceptance and general level of comfort with these technologies in the industry to assist in increasing their level of adoption.
Scope As open source languages and tools become more common across the data science landscape, opportunities to leverage these tools grows as well. Open source languages tend to be on the cutting edge, among other things enabling teams to explore the latest statistical techniques. R and Python are also prominent in the world of big data, connecting into powerful tools like Spark and Hadoop for distributed computing. Both of these languages also have impressive packages available in the world of machine learning and deep learning, bringing modern analytical tools to programming teams at little or no cost. Furthermore, R offers Shiny and R Markdown as advanced reporting tools enabling delivery of static or dynamic data dashboards. At the most basic levels, Python offers automation capabilities – and it is likely already available in most programming environments. These technologies have very much to offer, before even getting close to considering them as an organization’s submission or reporting language of choice.
This project will investigate the methods an organization can use to ensure the thoughtful use of these tools within regulatory environments. These methods will include the proper installation and deployment of open-source languages, validation of frameworks and packages used in development and analysis, quality control of in-house software development, and assurance of GxP compliance. Topics will be discussed at a higher level, veering the basic concepts behind techniques for installation deployment, and validation, as well as the specifics of how these things can be put into practice. The project will walk through proper installation the choice of development environment, and the implications of those choices. The risks of misunderstand what these tools can do given the environment they have been implemented in can be subtle and result in unexpected failures of applications, therefore proper validation is essential within a regulated space
The third element of this project involves the provision of best practices and guidelines that practitioners can use as resources when working with open source technologies in clinical research. One example will develop PHUSE specific coding style guides for open source programming languages, built on Google’s R Style guide, the tidyverse style guide, or for Python, the PEP-8 Style Guide. Most clinical programming departments have well defined Good Programming Practices (GPP) for SAS programming, as does PHUSE, and adoption of open source languages in the pharmaceutical industry will require similar guidance. A second example will provide guidance around the identification of common packages or common repositories that are determined safe to use within a programming environments when reporting on clinical trials, leveraging the work already being done by PharmaR. Furthermore, a clear understanding of packages selection and version control is essential to this process. The open source nature of these technologies, juxtaposed against the high standards of validation within the industry, necessitates the establishment of a trust mechanism for any open source element of the technology.
|Gayathri Kolandaivelu||Sub-Team Lead||JNJ||Community Priority Pulse Survey|
|Frank Menius||Sub-Team Lead||YPrime|
|Eli Miller||Sub-Team Lead||Atorus Research|
|Michael Rimler||Sub-Team Lead||GSK|
|Michael Stackhouse||Sub-Team Lead||Atorus||US Connect 2020|
How to Get Involved
We have an All Hands Call on the First Tuesday of every month at 11am Eastern Time (New York).
A link to the meeting can be found here: https://global.gotomeeting.com/join/742139861
The all hands meeting is open to all who want to attend.
Please contact the Project Leads to be added to the calendar invite, mailing list, and other resources. email@example.com
Sub-teams will have meetings as needed and set by the team leads. Please contact sub-team leads to participate in projects.
To propose a new Sub-team or project please contact the Project Leads firstname.lastname@example.org and attend the All Hands Call.
Objectives and Timelines
List proposed project deliverables and timelines.
|Recruit Volunteers||July 1st 2019|
|Deliverable – Community Priority Pulse Survey||Sep|
|Deliverable – Paper Presentation||US PHUSE Connect 2020 (March 2020)|
|January||Paulo Bargo, J&J||R Training Organizational Framework|
|February||Internal||Presentation of the Survey Analysis Team|
|March||Katia Glass, Consultant||Open Source Technology in Clinical Research|
|April||Ellis Hughes, Fred Hutch||R Package Validation Frameworks|
|May||Doug Kelkhoff, Genentech||Phuse Docker Deliverables Pilot|
|June||Terek Peterson, YPrime||The Randomizer: An IRT Solution Using R|
Archive Meeting Minutes, recordings, and slides are collected on the PHUSE Teamwork application. Please contact the Project Leads email@example.com to become involved with the project and added to the Teamwork application and other resources.
Open Source Training and Knowledge Base
Delivery Sub-Teams: 1 Community Priority Pulse Survey; 2 US Connect 2020
|Name (Organisation)||Name (Organisation)|
|James Kim (Pfizer)||Benno Kurch (Covance)|
|Yuichi Nakajima (Novartis)||Gayathri Kolandaivelu Project Lead (JNJ) 1|
|Harivardhan Jampala (Covance)||James Gunter (Covance)|
|Matthew Travell (GSK)||Michael Rimler Project Lead (GSK) 2|
|Michael Stackhouse Project Lead (Atorus) 2||Nicholas Masel (JNJ)|
|Steven Nicholas (Covance)||Sonakshi Shankar (OCS Life Sciences for Danone Nutricia Utrecht)|
|Tulasi Marrapu (GSK)||Eli Miller Project Lead (Atorus)|
|Name (Organisation)||Focus Area|
|Andy Nicholls (GSK)||Statistical Data Sciences|
|Brian Di Pace (GSK)||Clinical Statistics|
|Eanna Kiely (ClinBuild)||Data Standards Governance|
|Frank Menius Project Lead (YPrime)||Clinical Programming, Data Science, and Data Standards|
|Terek Peterson (YPrime) 2||Clinical Analytics & Data Strategies|
|Tatiana Scetinina (AstraZeneca)||Clinical Programming / Data Science|
|Name (Organisation)||Name (Organisation)|
|Aldir Medeiros Filho||Bruce Wienckowski (GSK) 2|
|Hanming Tu (Frontage)||Hatim Qais (FDA)|
|Jorine Putter||Katja Glaß|
|Kristy Lauderdale (CSG)||Niels Gronning (SAS)|
|Naga Madeti (Covance)||Ajay Yalwar (Covance)|
|Nurcan Coskun||Russell Gibson (CROS NT)|
|Sangram Parbhane||V Ashwin (GSK)|
|Walter Jessen (LabCorp)||Yufei Du (GSK)|
|Douglas Kelkhoff (Gene)||Marko Zivkovic (Genesis)|
|Sam Hume (CDISC)||Martijn.X.Van- Beelen (GSK)|
|Teckla Akinyi (GDK)||Nithiya Ananthakrishnan (Algorics)|
|David Pressley (Clear Creek Analytics)||Amol Waykar (d-wise)|
|Vamshi Matta (Covance)||Jingyuan Chen (Roche)|
|Ross Didenko (Covance)||Sasikumar Palanisamy (Covance)|
|Srinivas Veeragoni (Bayer)||Tim Williams (UCB)|
|Anbu Damodaran (Covance)|