Our first virtual SDE (Single-Day-Event) concluded on October 23, 2020. We had 212 attendees from 6 countries, with 74 registered for the pre-conference seminar.
A big "Thank You" to our speakers and sponsors! You have helped make our first SDE virtual event a success!
To those registrants who have NOT yet completed the survey, please do so by November 8. Let us hear your comments and ideas for improving a planned hybrid (virtual & onsite) conference for 2021!
The recordings and slides from all of the presentations will be available to registered attendees from the first week of November through November 30, and the slides will be posted on the website after November 30. The recording files will be separated by presentation so that attendees can choose which talks to see. The link to access the presentations will be provided by email when the files are ready. Stay tuned.
Thursday, October 22, 2020 Pre-Conference Virtual Seminar
|Time (EDT)||Presentation (click for abstract)||Presenter (click for bio)|
|8:15 - 8:30 AM||Introduction|
|8:30 AM - 1:00 PM||CDISC ADaM – Implementation by Example||Richann Watson, DataRich Consulting|
|11:00 - 11:30 AM||Break|
Friday, October 23, 2020 Single-Day Event
Participating as a sponsor is a great way to market your company's product and services. If you sign up as a sponsor for this 2020 virtual event by September 25, you get our “2-for-1 Special” – your 2020 sponsorship is automatically extended to NC SDE 2021, an onsite event planned for October 29, 2021.
Seminar and Presentation AbstractsCDISC: Beyond the Standards
Diane Wold, CDISC
CDISC Implementers preparing e-submissions know they need to understand the models (SEND, CDASH, SDTM, ADaM, Define-XML) and the associated implementation guides, but may not be aware of other resource available on the CDISC website and wiki. This talk will include a tour of the reorganized CDISC website, highlighting features and content that have been added and are being expanded. These include materials in the members-only area, available to anyone who works for a CDISC member organization. The presentation will also touch on publicly available wiki content.
The talk will end with an overview of projects that CDISC teams and staff are currently working on. These include updates and supplements to the familiar standards as well as extensions of and additions to other resources.
More Traceability: Clarity in ADaM Metadata and Beyond
Richann Watson, DataRich Consulting
Wayne Zhong, Accretion Softworks
Daphne Ewing, CSL Behring
Jasmine Zhang, Boehringer Ingelheim
One of the fundamental principles of ADaM is that datasets and associated metadata must include traceability to facilitate the understanding of the relationships between analysis results, ADaM datasets, and SDTM datasets. The existing ADaM documents contain isolated elements of traceability, such as including SDTM sequence numbers, creating new records to capture derived analysis values, and providing excerpts of define.xml documentation.
An ADaM sub-team is currently developing a Traceability Examples Document with the goal of bringing these separate elements of traceability together and demonstrate how they function in detailed and complete examples. The examples cover a wide variety of practical scenarios; some expand on content from other CDISC documents, while others are developed specifically for the Traceability Examples Document. As members of the Traceability Examples ADaM sub-team, we are including in this PharmaSUG paper a selection of examples to show how traceability can bring transparency and clarity to your analyses.
Common Pinnacle 21 Report Issues: Shall We Document or Fix?
Ajay Gupta, PPD
Pinnacle 21, also previously known as OpenCDISC Validator, provides great compliance checks against CDISC outputs like SDTM, ADaM, SEND and Define.xml. This validation tool provides a report in Excel or CSV format which contains information as errors, warnings, and notices. At the initial stage of clinical programming when the data is not very clean, this report can sometimes be very large and tedious to review. If the programmer is fairly new to this report s/he might not be aware of some common issues and will have to fully depend on an experienced programmer to pave the road for them. Indirectly, this will add more review time in the budget and might distract the programmer from real issues which affect the data quality. In this presentation, I will discuss some common issues with the Pinnacle 21 report messages created from running against SDTM datasets and propose some solutions based on my experience. Also, I will discuss some scenarios when it is better to document the issue in reviewer’s guide than doing workaround programming. While the author totally agrees that there is no one fit for all solution, my intention is to provide programmers a direction which might help them to find the right solutions for their situation.
Next Innovation in Pharma - CDISC Data and Machine Learning
Kevin Lee, Genpact
The most popular buzz word nowadays in the technology world is “Machine Learning (ML).” Most economists and business experts foresee Machine Learning changing every aspect of our lives in the next 10 years through automating and optimizing processes. This is leading many organizations including drug companies to explore and implement Machine Learning on their own businesses.
The presentation will discuss how Machine Learning can lead the next innovation in pharma with CDISC data. The presentation will start with the introduction of most innovative companies and how they innovate and lead the industry using Machine Learning and data. Then, the presentation will show how pharma should learn from them to innovate using Machine Learning and CDISC data. The presentation will also introduce the basic concept of machine learning and the importance of data.
The presentation will show how CDISC data will be the perfect partner of Machine Learning for the next innovation in pharmaceutical industry. Finally, the presentation will discuss how biometric department can prepare the next innovation and lead this data-driven Machine Learning process in pharmaceutical industry.
A Codelist’s Journey From the CDISC Library to a Study Through Python
Mike Molter, PRA Health Sciences
The publication of the CDISC Library should be every programmer’s dream. The use of PDF-based Implementation Guidelines or even Excel files downloaded from the CDISC website always produced manual, non-automated hiccups to the process of standards implementation. The Library gets us one step closer to automation nirvana. In this presentation I will illustrate a small-scale proof-of-concept web application in which a study team member defines study controlled terminology subsets, not through tedious Excel operations such as copying and pasting, but rather through minimal checkbox selection of controlled terms presented to the user through a browser by an application that knows which codelists are associated with which CDISC variables. We’ll see how just a few lines of Python code can extract from the Library; how a few more can send contents of a codelist to an HTML form; and on the backend, how a few more can process the choices a user made. The purpose of this exercise is not to demonstrate a fully functioning production web application, but rather, to give the reader a sense of what is possible. Knowledge of basic Python objects such as lists and dictionaries is helpful, but not essential.
Standardization for COVID 19 Trials, Following Different Sets of Master Protocols, and Using IQVIA CRF Design, SDTM and ADaM Standard COVID Libraries
Gustav Bernard, IQVIA
Jim Beck, IQVIA
COVID 19 trials have started with high expected turnaround time for studies. What we have done at IQVIA is create a Standardized CRF Design and CDISC SDTM and ADaM COVID 19 Standard Libraries. For each, there have also been processes put in place to insure consistency against the expected standard that will be implemented. On the ADaM side, some additional processes have been put in place, for example, for automating parameter information, creation of criteria flags for BDS domains and creating CTCAE grading for ADLB.
Submit Study Data to FDA: Current Status and Upcoming Study Data Technical Rejection Criteria Enforcement
Ethan Chen, FDA
The purpose of this session is to update Industry on the area of Electronic Submissions to FDA and communicate upcoming enforcement of the requirement to submit study data in standardized format. FDA will walk though published documentation and tools to help industry successfully submit an eCTD submission containing study data. In addition, there remain some submission types which are not required in eCTD format (i.e. non-commercial IND). FDA CDER will introduce the recently expanded CDER NextGen Portal to accept these submission types.
CDISC ADaM – Implementation by Example
Richann Watson, DataRich Consulting
This course will provide a high-level overview of some of basic ADaM concepts. It is assumed that the attendee will have a fundamental knowledge of the different ADaM structures and principles. The primary focus of the course is to illustrate the implementation of some of the concepts found in both the ADaM Implementation Guide (ADaM IG) and the ADaM Structure for Occurrence Data (OCCDS) documents. Items covered include setting up subject level analysis data set (ADSL) for common trial designs and walking through the process of creating a basic data structure (BDS) starting from a simple BDS and building on it to structure a data set that will support one or more analyses. Additionally, the course will demonstrate how to implement some variables that are only found in OCCDS, such as the standardized MedDRA query (SMQ) and customized query (CQ) variables.
Materials: Printed copy of the slides set up for note taking.
SAS Software Packages: N/A
Intended Audience: Individuals who have a knowledge of different ADaM structures and principles
Length and Format: Full-day lecture
- ADaM Overview
- High-level overview of data structures
- Illustration of Common Trial Designs
- Illustration of a simple BDS
- Build on simple structure by adding additional variables such as
- Analysis Visit Windowing Variables
- Descriptor and Indicator Variables
- Category and Criterion Variables
- Illustration of a simple AE and CM data set
- Build on a simple AE data set by adding additional variables such as
- AEs of Special Interest Variables
- Occurrence Flag Variables