Standard Analyses & Code Sharing
Working Group Overview
|Mary Nilsson||Industry Co-lead and Steering Committee Liaison||Lilly||nilsson_mary_e (at) lilly.com|
|Hanming Tu||Industry Co-lead and Steering Committee Liaison||Frontage||htu (at) frontagelab.com|
|Analysis and Display White papers (Mary Nilsson)||This project includes the development of white papers that provide recommended Tables, Figures, and Listings for clinical trial study reports and submission documents. The intent is to begin the process of developing industry standards with respect to analysis and reporting for measurements that are common across clinical trials and across therapeutic areas. Script developers could then create scripts consistent with the recommendations for all to use, improving efficiency and safety signal detection.||Yes|
|Code Sharing (Repository) (Hanming Tu)||Establish and maintain a collaboration platform for leveraging crowd-sourcing to improve the content and implementation of analyses for medical research and leading to better data interpretations and increased efficiency in the clinical drug development and review processes.||Yes|
|Communication, Promotion and Education (Jared Slain and Wendy Dobson)||The success of working group “Standard Analyses and Code Sharing” relies on the acceptance, input, feedback and further development from/by the user community. A main priority will be to develop a Communication Plan that conceptualizes efficient ways to communicate working group progress and results, e.g. white papers, and the call for scripts. It will define target groups, timing, communication channels, and the presentation.||Yes|
|Best Practices for Quality Control and Validation (Maria Dalton and Jane Marrer)||Quality and accuracy is essential in the health and life sciences industries. Patients and regulators must be able to trust analyses of clinical data. This paper will provide an in-depth review of best practices for robust quality control. The scope of the paper will be quality control of analysis programming of clinical data in health and life sciences organisations. It is applicable to the organisations who produce analyses of clinical data, including Contract Research Organisations. The paper will not discuss oversight of outsourced programming||Yes|
|GPP in Macro Development (Ninan Luke)||Macros provide an effective way to automate and reuse code in a standard and consistent manner across SAS programs. This ability to reuse code means that the use of GPP is particularly important in macro code and we think that there is a need to develop a consensus and document good programming prairies specifically for macro programming.||Yes|
|Test Data Factory (Peter Schaefer)||Several CS Projects develop and specify medical research methods, features, or processes, and some even create software components or subsystems for common tasks in drug development. As part of these efforts, a variety of SDTM or ADaM test datasets are required. The typical fallback position of project teams is to use data from the CDISC pilot project and/or anonymized study data that are provided by project team members. The Test Data Factory project aims at providing test data formatted in SDTM and ADaM that support a more systematic and comprehensive testing of these concepts and scripts.||Yes|
|Key Links||Previously published deliverables & News||FYI|
Working Group Meeting Minutes
|Year 2013||Year 2014||Year 2015||Year 2016||Year 2017||Year 2018|
- P04: Legal ownership and issues in open source repository (Sally Cassells)
- Task completed, creating a summary is planned
- P05: Create templates and metadata for documenting scripts and coding practices (Jean-Marc Ferran and Eric Sun)
- Closed, remaining tasks are folded into RGI project.
- P06: Refine process for creating and editing scripts in Google Code (Kevin Kane)
- Closed, any remaining tasks moved to Project 03
Development_of_Standard_Scripts_for_Analysis_and_Programming - This contains the legacy working group information.
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