PhUSE Collaboration New Projects & Call for Leaders

The PhUSE Collaboration is accepting proposals for new projects. A proposal will address problems of significant relevance to computational science related to drug, biologic and device development and must meet all of the guidelines for projects within the collaboration, including the following mandatory requirements: 

  • The projects must address significant research issues relevant to computational science
  • The project must not attempt to address FDA policy issues
  • There must be at one Project Lead personally involved in planning and carrying out the project.
New Projects can be submitted anytime during the year, however, in order to be included in the March meeting, new projects must be submitted by end of the previous year. If chosen, the project team will prepare objectives and an agenda for a project team meeting at the Annual Computational Science Symposium in March of the following year. 

Projects of Particular Interest to the the Collaboration include:

  • List and prioritization of issues in drug, biologic and device development related to computational science
  • Identification of possible projects based on pressing issues in drug, biologic and device development related to computational science
  • Identification, prioritization and potential pilots of emerging tools and technologies
  • Projects that fall within the scope of existing defined working groups

Application Process

Complete a New Project Request

Completed forms should be sent as an email attachment to

Call for Volunteers - New Projects

Working Group & Project Lead
Project Title
Standard Analyses and Code Sharing
Peter Schaefer -

Optimising Use of Data Standards
Bahvin Busa -

Optimising Use of Data Standards Kamiar Hamidi -
Test Data Factory or TDF

SDTM/ADaM Implementaiton FAQ

Data Reviewers Guide in XML

This project is to provide SDTM and ADaM test datasets that will support a systematic approach to testing scripts and analysis concepts that other PhUSE projects are working on. The project activities will include defining and implementing scripts that create test datasets with specified features, using a simulation based approach and providing these datasets and documentation through the existing infrastructure, for eg Github.  If you wish to become a member of this project.

This project will look to understand standards implementation nuances that exist for specific elements/issues for a given standard, across available versions. A gap analysis/white paper (and perhaps poster) will be the ultimate deliverable(s) to highlight how one addresses a common theme. This will be important for Sponsors that are still transitioning to newer versions of standards (e.g. SDTM), as well as the vast majority who have to govern multiple versions, even for a single product or application.

This project will develop the Data Reviewers Guide (i.e, SDRG and ADRG) in an XML format for regulatory submissions. Furthermore, to identify and develop style sheets, elements and semantics capability. To also assess a more cohesive cross-documentation data exchange between the define.xml and data reviewers guide as an XML format. Please note, the project intends to use the existing Analysis Data Reviewers Guide (ADRG) and Study Data Reviewers Guide (SDRG) as its basis.

Want to Co-Lead a project? Great ideas waiting for Leaders

While working groups and projects continue to collaborate, new innovative ideas are generated by different teams. Unfortunately, the collaboration doesn't currently have individuals able to volunteer in a Co-Lead role to move these ideas forward.  Below is a list of potential projects, the associated Working Group and a description of each.  If one of these ideas interests you and you are able to volunteer to Lead, please contact

Project Ideas
Working Group

Best Practices of Data Collection Instructions

Optimising the Use of Data Standards
While CDASH provides standards for data collection, it does not include instructions associated with the data collection.  For some data domains (eg, adverse events, concomitant medications, study disposition, reasons for permanent discontinuations from study treatment), sharing best practices on collection instructions will potentially lead to increased efficiency (site personnel won't have to learn as many collection methods) and fewer queries (less confusion by site personnel), and will enable more standardisation in analyses and displays for data, common across therapeutic areas. Unnecessary variations in collection instructions are creating inefficiencies and limiting abilities to fully leverage data standards.  Collection and associated instructions may vary depending on whether the study is early vs late stage, as discussed in the February 2016 FDA guidance ('Determining the extent of safety data collection needed in late stage pre-market and post-approval clinical investigations'). However, reducing the extent of variation would be beneficial for medical research.

Do you have feedback to share? Got an idea? Want to comment on a project?

All comments and feedback welcome. Please contact

Current CS Working Groups

For information on current CS Working Groups, please visit the PhUSE Wiki page.