Our North Carolina SDE 2022 is scheduled for October 21, with pre-event seminars on October 20. Hope to see you in-person next year at RTP, NC!
Friday, October 29, 2021 Single-Day Event Presentations
Seminar and Presentation AbstractsThe 80/20 Rule: How AI-Driven Automation Can Improve Efficiency and Quality in Clinical Data Management
Michael Roberson, MaxisIT
MaxisIT will present a case study for how a mid-size sponsor adopted a simple AI-driven approach to ingesting clinical data for multiple applications, domains and across trials. An established data mapping and standardization strategy enables the system to map the majority of data items based on the system’s past experience. This approach is particularly valuable as the shift to decentralized trials introduces many new data sources and types. The presentation will describe the underlying metadata model, AI-driven mapping, the process of verifying new mapping, and the automated data refresh process.
By attending this presentation the audience, will learn how AI-driven data ingestion saves time, improves data quality and enables faster insight into on-going clinical trials. And how structured metadata and data standards improve the process of on-boarding new data sources, which reduces the cost and saves time for current and future data integrations.
Knowledge Graphs as a Foundation for the Analytics (R)Evolution
Tim Williams, UCB
Adoption of Graph technology is growing within the Pharmaceutical industry at a time when Machine Learning and Artificial Intelligence are poised to revolutionize analytics. Knowledge Graphs provide a strong foundation for both ML and AI. At a more rudimentary level, graph data improves data quality and data integration. The R platform provides many tools for working with graph data, from data conversion to analytics and visualization. This talk includes examples of how graph approaches are being used in the healthcare industry and beyond, including support for FAIR data principles and how the technology is both data-centric and patient-centric at the same time.
Using AI to Understand the Patient Voice
Michael Durwin, ICON
One of the greatest challenges to healthcare in general and to clinical trials specifically is to understand the Voice of the Patient; the direct narrative of their daily challenges, their sentiment regarding their illnesses and treatments, their behavior regarding medication and even the words and phrases patients use to discuss their symptoms. The increasing accuracy of artificial intelligence in social listening tools has allowed social intelligence scientists to understand the Patient Voice, not as filtered through care givers or healthcare providers but directly from patients. AI turns qualitative patient statements into massive amounts of quantitative data. This data in turn allows healthcare providers to monitor symptoms and even predict disease prior to diagnosis, pharmaceutical companies to recruit for clinical trials, and health organizations to be alerted to disease outbreaks via self-published public dialogue.
During this talk we will look at how AI is helping health organizations to sift through social and digital content to collect legacy and real-time data in order to develop strategic solutions to deal with the many challenges faced by patients and organizations regarding healthcare, specifically clinical trials.
I don’t work in the healthcare industry. Why should I attend?
Even if you don’t work in the healthcare industry, “patients” translates to “customers”. What social intelligence does for clinical trial recruitment can be directly applied to consumer conversion. Treatment perceptions are the same as product perceptions. And marketers have the same need to understand their customers’ behavior as healthcare providers and brands have for understanding what drives their patients’ decisions.
Identifying Sources of Bias in Machine Learning Models
Jim Box, SAS
Artificial Intelligence systems and Machine Learning models are having a dramatic impact on many industries. However, with every story of success, we are seeing instances of biased results doing real harm. In this session, we will look at some of the sources of bias and unexpected results, and explore ways to mitigate the negative impact of these models.
Python-izing the SAS Programmer: A Brief Introduction to the World of Objects
Mike Molter, LabCorp
As the industry looks more and more toward broadening its technological horizons, programmers accustomed to SAS are asking more questions about and experimenting more with open source languages such as Python. As with any other journey into an expansive wilderness, the question of where to start can be daunting. Wherever one does start, it doesn't usually take long to realize that when it comes to object-oriented languages, the comparison to SAS goes beyond superficial syntax differences into something much more fundamental. In this presentation we'll look for commonalities between two apparently very different worlds. The intention is to demonstrate that with a better understanding of objects, maybe the difference between these two worlds isn't so vast after all.
Building a Bigger Analytics Tent at CDER
Rachel Dlugash, FDA/CDER
Stephen Wilson, FDA/CDER
The Analytics and Informatics Staff (AIS) works within the Office of Biostatistics at FDA's Center for Drug Evaluation and Research (CDER) to help push for improvements and efficiencies associated the regulation of drugs and biologics. The AIS, though relatively new to the Agency, is deeply involved in a number of important projects to promote operational efficiency, support standardization and enhance/expand regulatory review processes at CDER. These activities include the creation of an AIS CDISC Data Standards Study Group for OB, the close collaborative development of high-priority COA/QRS supplements for the Agency, a pilot project to assess natural language processing (NLP) for information base development and the promotion of open source tools. We view this session as an opportunity for all of us to learn about each other and continue to work together to improve.
Avoiding ADaM Sinkholes
Richann Watson, DataRich Consulting
Karl Miller, Clinical Solutions Group, Inc.
The ADaM Implementation Guide was created in order to help maintain a consistency for the development of analysis data sets in the pharmaceutical industry. However, since its inception we have seen issues with guideline non-conformance which can impede this development process and carry impacts that are felt down-stream in subsequent processes. When working with ADaM data sets, non-compliance and other related issues are likely the number one source for numerous hours of re-work; not only creating unnecessary additional work for the data sets themselves, but also for reports, compliance checks, the Analysis Data Reviewers Guide (ADRG), etc. all the way down to the ISS/ISE processes. Considering this breadth of impact, one can see how devastating these sinkholes can be. Like any sinkhole, there is a way out of it but it is a long, tedious process that will consume a lot of resources and it is always better to avoid the sinkhole entirely. This presentation will assist you in creating compliant ADaM data sets, provide the reasoning on why you should avoid these sinkholes, all of which will help minimize re-work and likely eliminate the need for additional work.
The Analysis Result Standard Project
Diane Wold, CDISC
Jeff Abolafia, Pinnacle 21
The presentation will start with a high-level update on current CDISC activities, including the development of extended Analysis Results Metadata (ARM). It will continue with detail on the progress of the ARM project. The aims of the project are to 1) extend the current Analysis Results metadata to improve traceability and facilitate TFL automation and 2) develop an Analysis Results Data Model for storing analysis results.
Generating .xpt Files with SAS, R and Python
Todd Case, Vertex Pharmaceuticals
YuTing Tian, Vertex Pharmaceuticals
The primary purpose of this paper is to first lay out a process of generating a simplified Transport (.xpt) file with RStudio and Python to meet study electronic data submission requirements of the Food & Drug Administration (FDA). The second purpose of this paper is to compare the .xpt files created from three different languages: R, Python and SAS. The paper is the expansion of the original FDA guideline document “CREATING SIMPLIFIED TS.XPT FILES”, published in November, 2019. Transport files can be created by SAS, as well as open source software, including R and Python. According to the FDA guideline document mentioned above, .xpt files can be created by R and Python. This may allow Pharmaceutical companies to expand use of R and Python beyond data visualization and statistical analysis currently being generated by these two languages. Hopefully, readers can use the process shown in the paper as a template to create .xpt files.
Industry Projects on the Validation of Open-Source
Michael Stackhouse, Atorus
The world of open-source in the pharmaceutical industry has rapidly evolved over the last few years. Greater focus on the enablement of open-source languages for regulatory submissions has lead to the exciting new developments. These developments include industry specific working groups, focusing on different challenges of open-source, and open-source packages focused on clinical submission activities. A key area of focus for these efforts has been the topic of validation. Languages like R and Python bring new challenges in the area of validation, different than those of which the industry is accustomed. This presentation will provide an overview of several different ongoing efforts tackling these challenges in industry across organizations such as PHUSE and R Consortium.
CDISC ADaM – Principles, Rules and Complex Examples
Richann Watson, DataRich Consulting
This course will provide a high-level overview of some of the basic ADaM concepts; however, it is assumed that the attendee will be familiar with the different ADaM structures and principles. The course will delve into what is meant by traceability and analysis ready as well as look at some rules and best practices. However, the primary focus is to illustrate the implementation of some of the more difficult or less common concepts found in both the ADaM Implementation Guide (ADaMIG) and the ADaM Structure for Occurrence Data (OCCDS) documents. The course includes an illustration of the use of criterion variables (CRITy and MCRITy) and record-level and parameter-level population flags (-RFL and -PFL), as well as a demonstration of how to set up time-to-event and questionnaire/rating/scales analysis data sets. In addition, it will go into depth about AEs of special interest and the use of Standard MedDRA Queries (SMQ) and provide an illustration of how the OCCDS can be used to handle the non-typical analysis for events data.
Why you should take this seminar if you work with ADaM:
- You have an understanding of basic ADaM structures and principals, but those nuances have a tendency to trip you up or maybe you just need a refresher on standards and best practices.
- You have been asked to implement some less common or more difficult concepts, such as criterion variables (CRITy/MCRITy), record-/parameter-level population flags (-RFL/-PFL).
- You are tasked to create a data set that deals with adverse events of special interest (AESI) or a non-typical analysis of events.
- You need to set up time-to-event, questionnaire, rating and/or scales analysis data set, and would like to use the most effective techniques.