PHUSE EU CONNECT 2022
The PHUSE EU Connect 2022 is being held November 13th–16th. OCS Life Sciences has submitted five abstracts. Although these are not accepted yet, we understand you are inquisitive about our topics for this year. So, feel free to read more about them below. If you have any questions, you can contact us.
SanityCheckR: data quality control with R Shiny
by Diana L. Santos Ferreira
Data validation is a critical step in any statistical programmer workflow and performing it using SAS software implies writing many lines of code which can be time consuming. With the aim of empowering statistical programmers to interact in a quick and easy fashion with data, we developed SanityCheckR an interactive application to explore, visualize and summarize SAS dataset files, using R language and the Shiny framework. The dashboard was customized to allow the user to interact with the dataset (for example, dynamically filtering variables) and provide easy access to the most common quality control tools, such as descriptive and pivot tables, plots to visualize variables and reveal missing data patterns, accelerating and simplifying the validation process. All outputs can be downloaded and therefore high-quality documents can be swiftly shared with team members and clients.
The impact of migrating to SAS Viya 202X (aka SAS Viya 4)
by Juan Sanchez and Daniel Christen (SAS)
An Automated Approach to Assign Job Inputs Dynamically in LSAF
by Kai Wanke
The Life Science Analytical Framework (LSAF) uses job files to configure and execute SAS programs, and to move the necessary files from a stable repository to a workspace where they can be processed before being moved back into the repository.
Typically, the input files are either added manually by the user or taken from a template job.
Both approaches can be cumbersome in cases where large numbers of files from different locations are processed, especially if the input files differ between job executions.
Here, I present an approach using LSAF APIs to handle the input locations of a job dynamically.
This method allows dealing with large and changing numbers of files stored in a fixed folder structure without any manual input, making it highly flexible and reusable over time.
This approach allows the user to access and summarize files generated in multiple trials with ease and minimal manual input.
Do It Yourself: Case Report Form (CRF) Annotation
by Caro Sluijter
Maturing standardisation; the switch from spreadsheet-based standards to an MDR
by Louella Schoemacher & Jasmine Kestemont
In order to work consistent, structured, and time efficient throughout multiple studies, standardisation is key. Standardisation can be relatively simple with having the company standards in a spreadsheet and using this spreadsheet to share study-specific metadata requirements. However, with a growing number of compounds, indications, studies, and number of CRO’s involved in creation of the SDTM package (aCRF, SDTM datasets Define-XML and SDRG), it gets harder and more important to have and maintain the standards. Furthermore, individual studies can have specific requirements which may result in deviations even within a compound or indication. When the number of studies grows, a simple spreadsheet may no longer be sufficient to capture all subsets of metadata requirements. When the standards cannot keep up with the growing number of studies, this can result in an overgrowth of different annotated CRFs and Define-XML files leading to undesirable inconsistencies.
CALL EXECUTE will FIGURE it out: Data-Driven Figure Programming
by Mitchikou Tseng