PHUSE EU Connect 2025
On 16 – 19 November 2025, the PHUSE EU Connect 2025 will take place in Hamburg. We’re pleased to be attending this event. It offers a unique opportunity to explore the latest trends, insights, and technologies shaping the future of the industry. In addition, our colleagues have submitted several abstracts for this event.
%batchrunprograms:
SAS Studio Background Submit on Multiple Programs
by Luis Andre Soriano
As with traditional local SAS Batch Run, SAS Studio Background Submit on a program is important in ensuring the program runs successfully from a clean state and contains no errors, warnings, or undesirable notes.
Often, multiple programs need to be run sequentially, independently, and frequently (e.g. developing SDTMs/ADaMs/TLFs). There is unfortunately no built-in way in SAS Studio to Background Submit multiple programs. Current solutions involve tinkering with XCMD option (security risk), creating non-SAS tools (separate skill to learn), or using %include on multiple programs (independence hard to ensure).
In this presentation we'll show that %batchrunprograms is developed by only incorporating tools from SAS/CONNECT and Base SAS (e.g. ODS, PROC PRINTTO). Users can setup maintained lists of programs that can be run sequentially and independently any time, with functionalities of stopping the batch run in case of errors, skipping some programs, and providing users summary of the batch run.
Enhancing Clinical Trial Management
with Automated Data Integration and
Real-Time Analytics
by Emmy Pahmer and Katrijn Op De Beeck (argenx)
Argenx is a biotech company managing multiple compounds across different phases of development, working with various CROs. One key challenge has been achieving unified, timely oversight of operational performance and trial progress across portfolios. To address this, we developed a data mapping framework in SAS Life Science Analytics Framework (LSAF). Vendor data ingestion is set up in collaboration with each vendor and the operational data is standardized internally. Another team generates automated SDTM datasets. With both the clinical and operational data, we can generate important study metrics which are then used to create visualizations designed to meet the needs of our internal stakeholders. This fully automated integration enables daily-refreshed, cross-study and cross-vendor analytics, driving better visibility into study progress, vendor performance, and protocol compliance. We will share how this architecture was developed, the tools and processes we employed, and how our visualization strategy enables meaningful insights across our clinical programs.
Smarter SAS logging—with a macro AI hasn’t mastered (yet)
by Jules van der Zalm
Program logs are vital for debugging. SAS generates solid log files, but they’re not always great for pinpointing the exact location of problematic code—especially when it’s inside a macro.
It’s good practice for SAS macros to announce themselves in the log. By printing their name, it becomes much simpler to trace the source of an error message. These announcements often include the macro name as well as parameter names and values.
Maintaining these log statements is tedious and error-prone. They’re often copied from program to program, leading to inconsistencies between what’s logged and what the macro is actually doing.
In this paper, I’ll present and share a SAS macro – that AI is not able to produce (yet) – that automates this process. Just call it at the top of your macro, with no extra parameters, and your macro will log its own name and parameter values automatically.