Standard Analyses & Code Sharing
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Analysis and Display White Papers: This project includes the development of White Papers that provide recommended Tables, Figures and Listings for clinical trials 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.
Code Sharing (Repository):
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. Our vision is to change from everyone building one's own tools to people collaboratively building shared tools and reusable code library through a collaboration platform.
|Communications, Promotion and Education: The success of the Working Group 'Standard Analyses and Code Sharing' 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. White Papers and the call for Scripts. It will define target groups, timing, communication channels and the presentation.
Best Practices for Quality Control and Validation:
We propose to create a subgroup of the Good Programming Practices workgroup to write a white paper on the topic of Best Practices for Quality Control. 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.
GPP In Macro Development:
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.
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 Macro
- Best practices while writing Macro
- Structured documentation of Macro
- Optimisation and saving compiling time while using Macro
- Refactoring in Macro
Test Dataset Factory: 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 anonymised 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.