Wednesday, October 24th, 2018
12:30 PM (Registration)
1:30 – 4:30 PM (Meeting)
Meeting Center Space C
309 Vintage Park Way
Foster City, CA 94404
Thomas Leung (415) 956-3611
Prabhakara Burma, Lead SAS Programmer, Acerta Pharma
“A Macro to Avoid P21 FDAC036 Error Message for Regulatory Submission Datasets”
As per the CDISC guidelines the clinical trials data must be submitted in SAS® V5 Transport file format. These transport files should be validated using the Pinnacle 21 (P21) validator to ensure CDISC compliance and all errors given by the P21 validator should be addressed by the programming team. One of the most common P21 error messages is FDAC036 (Variable length is too long for actual data).
Usually for most of the character variables, we typically assign some default length while creating SDTM and ADaM datasets. But if the variable length is greater than its maximum value length, P21 validator will produce an error message (FDAC036). To address FDAC036 error message as well as significantly decrease the size of the file, we developed a macro named ADJLEN, which will be discussed in this paper.
About the Speaker:
My name is Prabhakara, Burma. I am working as a lead SAS programmer at Acerta Pharma as a consultant. I have 8 plus years of pharma experience. My major experience is with the oncology therapeutic area. I have finished my masters from University of Hawaii at Manoa.
Janet Li, Statistical Programmer, Pfizer
“Something Old, Something New: A little programming management can go a long way”
Have you ever found yourself searching tirelessly for documents as the lead programmer of a study when the previous study lead had already left? Or maybe you were in the shoes of a manager who wished for easier access to the status of the projects that your direct reports lead? Statistical programmers in the pharmaceutical and medical device industry work on interdisciplinary and cross-functional teams.
A successful statistical programmer must not only have proficient programming knowledge, but also have strong organizational and communication skills. We should be keeping track of what we as programmers are doing ourselves but also what others on the study team and project are doing. We propose a project management and tracking tool to help manage ourselves, others, deliverables, and timelines at the study level.
The tool includes but is not limited to study contact information, location of study files (i.e. raw datasets, SDTM, ADaM, mock-ups), future and completed deliverables, study highlights and risk management. A summary of study highlights can be pulled across the multiple study tracker files and be presented as an aggregate report to upper management, such as the head of programming. The tool we present can be updated to fit the needs of any statistical programming organization or team.
About the Speaker:
Janet is currently a Clinical Programmer II at Pfizer in San Francisco, CA. Janet has over 5 years of experience working as a project manager/research coordinator for stroke-related observational studies and clinical trials at Georgetown University.
She has also been conducting research on statistical considerations for the master protocol design being implemented in oncology trials. She holds a BS in Neuroscience from Duke University and a MS in Biostatistics from Georgetown University. In her spare time, she loves to read (especially Murakami novels) and explore new places.
Charu Shankar, SAS Senior Technical Training Specialist, SAS Education
“Top 10 Coding Efficiency Hacks”
What is the data workers #1 rule? How can you save on cpu, i/o, memory, storage and programmer time.
Come to this informative session to learn te top 10 sas coding hacks that can save you time and money.
About the Speaker:
SAS Senior Technical Training Specialist, Charu Shankar, teaches by engaging with logic, visuals and analogies to spark critical thinking. She interviews users to recommend the right SAS training. She is a frequent blogger for the SAS Training Post. When she’s not teaching technology, Charu is passionate about helping people come alive with yoga and is a food blogger.
She also helps support candidates who are looking to land work using SAS through this Linkedin Group. Charu has presented at over 100 SAS international user group conferences on topics related to SAS programming, SQL, DS2 programming, tips and tricks, efficiencies, new features of SAS and SAS Enterprise Guide.
Adarsh Reddy, Senior Statistical Programmer, Gilead Sciences
“A Concept of a Macro to Summarize the Proc Compare”
Imagine a traditional Phase 3 study with 40 SDTMs, 10 ADaMs and 250 ~ 300 Tables, Listings and Figures. By far the majority of the statistical programming teams in the industry use PROC COMPARE to validate the Datasets and TLF’s. And almost all the teams use some kind of a tracker to store the status of the validation. Keeping the tracker up-to-date is a huge task on its own.
After all the development and validation, the team has sent the TFLs for the review of Statistician. The statistician points out a minor flaw in the mapping/programming of the SDTM.DM, fixing which warrants a full rerun. Once the rerun is run, the team has to go through the motion of opening and verifying all the ~350 proc compare outputs (just to make sure) and entering it in the tracker.
How awesome it would be, to have a macro output a tracker for you. I would like to show a short demonstration of such macro.
About the Speaker:
I’m Adarsh Reddy and I’m currently working as a Senior Statistical Programmer in Oncology Therapeutic Area of Gilead Sciences. I have been with Gilead for 2 years and 11 months and before that I was with a large CRO called INC Research (currently Syneos Health) in NC for about 4.5 years and even before that I was with Amylin Pharmaceuticals in San Diego for about 1 year.
I have a Masters from University of Texas in Tyler.