CSS Working Groups
This wiki is available to document the progress of the PhUSE Computational Science (CS) working groups and projects. The wiki enables quick,easy and transparent online collaboration. Working groups each have a main page which is the starting page for all Working Group activity.
If you are new to wikis you might want to check out the help section. To get started click on your working group below.
For instructions on how to subscribe to the working group/project email list or contact a working goup leadership team click here
The Working Groups are governed by the Computational Science Steering Committee which provides oversight to the Working Groups.
Useful Information for Projects
Brand New: PhUSE has been working on producing a Standard Operating Procedure for Version Control of PhUSE Deliverables. We are pleased to announce this has now been agreed by the Steering Committee and PhUSE Board of Directors. Moving forward, we ask all members that are working towards a deliverable for review and approval, to start using this agreed process. If you have any queries, please reach out to your Working Group Lead.
- This is the flow diagram
- This is the Working Practice Procedure
Have a Project idea?
Complete the New Project Request template for submission to the Steering Committee through your Working Group Lead
Giving a PhUSE Presentation?
Here is the link for PhUSE CS Power Point Template
Writing a White Paper / PhUSE Deliverable?
Use this PhUSE link to get you going
Need to create a Wiki project area?
Use this link to start promoting the work your project is doing
Call for Volunteers
Need additional support? People to join the project? Contact wendy(at)phuse.eu who can advertise through the PhUSE media outlets
Overview Of PhUSE Structure And Projects
PhUSE Working Groups
Optimizing the Use of Data Standards The development and adoption of data standards over the last decade has shown significant promise in improving efficiencies in the product submission and review process. However, there have also been gaps, issues and challenges in the interpretation and use of the standards. This group will identify specific gaps preventing FDA and industry from optimizing the use of standards and collaborate to close those gaps. Here is a list of our current projects.
Best Practices for Data Collection Instructions
Best Practices for Metadata Documentation (define.xml versus reviewer's guide)
CDISC Implementation Primer
Clinical Legacy Data Conversion Plan & Report
Data Reviewer's Guide in XML
Data Standards for Observational Studies
Define-XML V2.0 Completion Guidelines and Stylesheet Recommendations
Industry Experiences Submitting Standardised Study Data to Regulatory Authorities
SDTM ADaM Implementation FAQ
Standard Analyses and Code Sharing Leverage crowd-sourcing to improve the content and implementation of analyses for medical research, leading to better data interpretations and increased efficiency in the clinical drug development and review processes. Here is a list of our current projects.
Analysis and Display White papers
Code Sharing (Repository)
Communication, Promotion and Education
Test Dataset Factory
Standard Analyses Deliverables & Key Links
There is a need to improve nonclinical assessment and regulatory science by identifying key needs and challenges in the field and then establish an innovative framework for addressing them in a collaborative manner. The group created a framework for moving certain projects forward to support nonclinical informatics efforts and to develop specific implementation solutions and SEND. Here is a list of our current projects.
Data Consistency: SEND Datasets and the Study Report
Data Visualisation as an Enabler for Nonclinical Safety Signal Detection
Demystifying Define-XML Codelist Handling for Nonclinical Studies
Industry SEND Progress Survey
Modeling Endpoints: How to Model Anti-Drug Antibody Data in Nonclinical Studies
Nonclinical Script Assessment Project
Nonclinical Study Data Reviewers Guide
SEND Implementation User Group
Understanding RDF/Linked Data for Potential Non-Clinical Use
Emerging Trends and Technologies This emerging technologies working group will be an open, transparent forum for sharing pre-competitive means of applying new technologies and is being challenged with creation of well-defined collaborative projects that will describe, prioritize, assess, and assist advancement of these opportunities. Possible topics include (but are not limited to) semantic web applications, analysis metadata, modeling, simulation, and “The Cloud”. Here is a list of our current projects.
Clinical Trials Data as RDF
Cloud Adoption in the Life Sciences Industry
Data Visualizations for Clinical Data
Introduction to Clinical Development Design (CDD) Framework
Investigating the use of FHIR in Clinical Research
Key Performance Indicators & Metrics (KPI)
Educating For The Future
Methodologies and technologies utilized in the clinical development process have been largely unchanged since 2005. Externally, there have been immense advancements in technology and mindsets related to working with data that have had little impact in our industry. This Working Group sees this as a risk. The risks breakdown into 3 categories:
1) There is a risk of advancement happening outside of our domain that may expose us to threats that we are unable to protect ourselves against.
2) There is a risk that our skill sets and capabilities become outdated and external forces strongly influence the Industry without us having a say
3) There is a risk that we are missing opportunities to become more efficient and to use these advancements to deliver better value for patients
The goal of this Working Group is to develop frameworks by which to educate the PhUSE community at large. The frameworks will be designed to educate the community on the importance of topics where we feel we have gaps, the details of the topics themselves, and how they can be used to drive innovation in the industry.
Machine Learning / Artificial Intelligence
In this new era of data transparency and sharing data with researchers, healthcare companies are defining their processes and de-identification guidance in order to comply with data privacy regulations. In particular, it is possible for researchers to request access to data across sponsors. Over twenty participants from Pharmaceuticals, CROs, Software and Academia, as well as CDISC and Data Privacy experts, have collaborated to define a set of rules built around the CDISC SDTM standards to provide the industry with a consistent approach to data de-identification and increase consistency across anonymized datasets.
Clinical Trials Toolkit
Data Offset Algorithm
GDPR Impact on Data Collection Practices
PhUSE Archived Working Groups & Projects
FDA comments are an informal communication and represent the individual's best judgment. These comments do not bind or obligate FDA. The contents of this wiki are from the individual contributors and do not necessarily reflect the view and/or policies of the Food and Drug Administration, the employers of the individuals involved or any of their staff.