We are pleased to announce that 4 abstracts and 1 poster presentation of OCS Life Sciences have been approved for the PHUSE EU Connect 2022. Hopefully, we can travel to Belfast with a large delegation in November. 

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 Institute)

Currently, most of the SAS-based Statistical Computing Environments (SCEs) are built on the SAS 9 version; some on SAS Viya 3. Recently, SAS Institute released its modern cloud-native, unified version with multi-lingual programming capabilities, SAS Viya 202X (Viya 4).

This journey to SAS Viya 4 brings specific challenges for GxP environments from a technical and change management point of view. Due to the compliance aspects, this transition can take up 2 to 3 years.

This proceeding aims to provide a bird’s-eye view over the considerations to be made from different angles in order to adopt SAS Viya 4: the business and industry point of view; can SAS Viya 4 be a validated environment and how it can be validated; the considerations towards planning and project management; and last but not the least, the scenarios made available by SAS Institute as solutions to such requirements, drafting their pros and cons.

Maturing standardisation; the switch from spreadsheet-based standards to an MDR

by Louella Schoemacher & Jasmine Kestemont (Innovion)

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

In studies with multiple parameters of interest taken at varying timepoints under different treatment groups, parts, or periods in the trial, BY statement and/or employing SAS MACRO in SAS GRAPH are sometimes not convenient in the creation of repeated figure outputs. But, by incorporating CALL EXECUTE in our figure programming, we can successfully create repeating graphs for which input values are data driven and adding specific conditional attributes are flexible to the user.

This paper will discuss a data-driven figure programming approach in the following steps: 1) Creation of a macro for your figure, 2) Addition of new variable(s) to your input dataset for possible specific tweaks to your figure and 3) Making use of DATA STEP + CALL EXECUTE to robustly call the figure macro and apply conditional tweaks to your output.


Do It Yourself: Case Report Form (CRF) Annotation

by Caro Sluijter

There is more than one way to skin a cat; and there is also more than one way to annotate a Case Report Form (CRF).
As the regulatory authorities and CDISC have guidelines to deal with metadata submission, we do have a roadmap to create an annotated CRF. However, some of these guidelines contradict each other, and what do you do when your CRF cannot adhere to all the guidelines?