Standard Analyses & Code Sharing

Working Group & Project Scope

Working Group Leads 

Mary Nilsson

Hanming Tu

The development and implementation of industry standards provides a great opportunity to develop standard reporting across industry and to support the needs of the FDA. This Working Group will develop recommendations for analyses and displays in areas that could benefit from crowd-sourcing and maintain a publicly available repository for storing program codes to be used as analytical tools for medical research.

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.

Best Practices for Quality Control and Validation

Maria Dalton

Jane Marrer

This project proposes creating a sub-group of the Good Programming Practices Working Group project to write a white paper on the topic of Best Practices for Quality Control. Quality and accuracy are 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, applicable to organisations which produce analyses of clinical data, including contract research organisations. The paper will not discuss oversight of outsourced programming.

Code Sharing

Hanming Tu

Bob Friedman

(Repository): This project aims to establish and maintain a collaboration platform for leveraging 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. Our vision is to change from everyone building one's own tools to people collaboratively building shared tools and a reusable code library through a collaboration platform.

Communications, Promotion and Education:

Wendy Dobson

Jared Slain

The success of the Standard Analyses & Code Sharing Working Group relies on the acceptance, input, feedback and further development from/by the user community. The Communications Plan conceptualises efficient ways to communicate Working Group progress and results, e.g. via white papers and the call for scripts. This project defines target groups, timing, communication channels and the presentation.

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 practices specifically for macro programming.This project intends to create a guideline/white paper for creating well-structured and precisely documented macro code that will be easy to read and maintain over time. The proposed white paper will primarily describe:

  • coding style of macros

  • best practices while writing macros

  • structured documentation of macros

  • optimisation and saving compiling time while using macros

  • refactoring in macros. 

Test Dataset Factory: 

Dante Di Tommaso

Several Working Group projects develop and specify medical research methods, features, or processes, and some even create software components or sub-systems 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 anonymised study data that are provided by project team members. The Test Dataset Factory project aims to provide test data formatted in SDTM and ADaM that support a more systematic and comprehensive testing of these concepts and scripts.



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