Check out this select sampling of some of the phenomenal SAS and open source presentations, demos, and workshops that will be offered at WUSS 2026 by technical experts and industry leaders!
In addition, the full list of WUSS 2026 paper abstracts is now available!
From ChatGPT to Copilot, From Prompts to Workflows: Putting AI to Work Across the Clinical Programming Lifecycle
Lida Gharibvand
The way clinical programmers use AI has shifted fundamentally. What began in 2025 as standalone, prompt-driven experimentation — copying code into a chat window on the side — has matured into always-on, context-aware collaboration embedded directly in the tools we already use. This presentation traces that arc and offers a practical roadmap for where AI now fits in regulated SAS workflows, and where it is heading next.
We distinguish the three kinds of AI that matter to our work — generative, predictive, and agentic — and examine the 2026 shift from embedded copilots toward governed, accountable agents. Using SAS Viya Copilot and the Azure + LLM architecture behind it as a worked example, we walk a natural-language prompt through to runnable SAS code, executed output, and summarized insights, and compare the leading assistants (ChatGPT, GitHub Copilot, Microsoft Copilot, and SAS Viya Copilot) for statistical programming teams.
Throughout, the emphasis is on discipline rather than novelty: where AI genuinely accelerates the draft across the lifecycle — SAP and spec review, SDTM/ADaM derivation, TFL QC, documentation, and submission — and where human accountability remains non-negotiable. We close with the current regulatory picture (the FDA credibility framework and the 2026 FDA–EMA Good AI Practice principles), a quality-gate checklist, and a practical risks-and-mitigation matrix. The goal is not risk-free AI — it is controlled, reviewable, value-adding AI.
About the Presenter: Lida Gharibvand, Ph.D., M.S. is a Distinguished Professor and Director of Statistics and Research Education in the Faculty of Graduate Studies at Loma Linda University, School of Allied Health Professions. She is widely recognized for her contributions to biostatistics, research methodology, and clinical research support across health sciences.
She has played a key role in mentoring and supporting numerous Ph.D. students, guiding their training in statistics, research design, and data analysis, and helping develop the next generation of researchers and scholars.
In addition to her academic leadership, she serves as President of the Orange County and Inland Empire SAS Users Group (OCIE SUG) and is a Leadership Council member of the Orange County and Long Beach Chapter of the American Statistical Association (ASA), where she promotes statistical education and professional collaboration across academic and clinical communities.
Charting Your AI Journey: A Practical Roadmap from Machine Learning Foundations to Generative and Agentic AI
Ryan Paul Lafler
Artificial Intelligence (AI) continues to reshape business, technology, science, and research by enabling systems to learn from data, automate workflows, and support more adaptive decision-making. This paper presents a practical roadmap for understanding AI through the progression from machine learning (ML) foundations to modern generative and agentic AI systems. It introduces AI as a broad field and machine learning as a data-driven approach to AI, then examines supervised learning for predictive analytics on labeled data, unsupervised learning for pattern discovery in unlabeled data, and generative learning for representation learning, synthesis, reasoning, and content generation. The paper introduces key algorithms, architectures, use cases, model hyperparameters, and practical considerations such as overfitting, underfitting, and model evaluation to support applied implementation across industry settings. It concludes by connecting deep learning with modern generative model architectures, including encoder models, decoder-only large language models, and encoder-decoder models, to the emerging shift toward agentic AI systems that combine models with retrieval, tools, APIs, and workflow orchestration to support practical industry use cases in 2026 and beyond.
About the Presenter: Ryan Paul Lafler is the Founder, CEO, and Lead Consultant of Premier Analytics Consulting, LLC, a California-certified small business based in San Diego specializing in applied AI/ML systems, data infrastructure engineering, advanced statistical analysis, enterprise GIS modernization, and custom full-stack analytics architecture. As a principal architect and consultant, Ryan designs, delivers, and supports infrastructure-aware AI/ML solutions, production-grade analytics platforms, open-source modernization efforts, scalable data engineering infrastructure, GIS and spatial analytics systems, and statistical modeling workflows for enterprise organizations, public-sector agencies, and research institutions. Through consulting and contracting roles, he has cross-industry expertise in AI frameworks and programming languages including Python, R, SQL/NoSQL, SAS®, and modern JavaScript frameworks, and implements structured quality control, validation, and governance practices for automated analytics and AI-assisted workflows. He also serves as an Adjunct Professor in the Big Data Analytics Graduate Program, the Department of Mathematics and Statistics, and the Global Campus Program at San Diego State University. He earned his Master of Science in Big Data Analytics (2023) following the defense and publication of his thesis, and his Bachelor of Science in Statistics with a Minor in Quantitative Economics (2020), both from San Diego State University.
Claude the SAS Programmer: Agentic AI creates SAS 9 code before your eyes
Joe Matise
AI innovation is accelerating — and SAS 9 isn’t getting left behind. In this session, learn how Agentic AI can now generate SAS 9 code using the SAS Visual Studio Code Extension and the SAS9py package. You’ll see how to set yourself — and your team — up for success using code extensions, streamlined workflows, and automation through agent-generated scripts. No prior experience with SASPy, Agentic AI, or VS Code required. Whether you run SAS locally or on a server, this session is designed to help any programmer benefit from trusted, explainable AI assistance.
About the Presenter: Joe Matise has been a SAS developer for twenty years, working to improve access to data and support researchers in his organizations. His current passions are cloud and AI development. In his spare time, he spends time with his wife and two teenage sons. His favorite Pokémon is Metagross.
Macro-Powered Clinical Reporting in SAS: Automating Workflows with Automatic Macro Variables, ODS TRACE, and Reusable Table-Building Macros
Chary Akmyradov
SAS macros are often introduced as a way to reduce repetitive code, but their greatest value comes from building reusable, auditable, and scalable programming workflows. This paper presents a practical approach to using the SAS macro facility to automate clinical and healthcare reporting tasks, with examples drawn from real-world statistical programming and biostatistics workflows.
The presentation begins with a brief review of macro variables, macro parameters, macro debugging options, and reusable macro design. It then demonstrates how automatic macro variables generated by SAS, SAS Enterprise Guide, and related execution environments can be used to detect project context, initialize libraries, standardize folder structures, and reduce hard-coded paths. Special attention is given to project startup automation, including Enterprise Guide autoexec-style workflows and portable strategies for SAS Studio and SAS Viya.
The paper also demonstrates how to use ODS TRACE ON to discover procedure output object names and ODS OUTPUT to capture selected results into SAS data sets. These captured outputs can then be wrapped into custom macro functions to produce reusable clinical summary tables, including frequency summaries, chi-square and Fisher exact test results, t-test outputs, Wilcoxon summaries, and Table 1-style reporting structures.
The presentation concludes with practical recommendations for macro validation, documentation, portability, and responsible use of AI to support macro development, code review, and teaching. Attendees will leave with a framework for moving from individual SAS programs toward reusable macro-powered reporting systems.
About the Presenter: Dr. Chary Akmyradov is a Lead Biostatistician at the Children’s Nutrition Research Center, Baylor College of Medicine. He has extensive experience in applied statistics, clinical and biomedical research, statistical consulting, and SAS programming. His work focuses on helping investigators design studies, analyze complex healthcare data, and produce reproducible, publication-ready statistical deliverables.
Dr. Akmyradov has developed SAS macro programs over many years to automate repetitive analyses, streamline clinical reporting, generate custom summary tables, and improve consistency across statistical workflows. His programming interests include macro automation, ODS-based reporting, metadata-driven programming, project startup workflows, and efficient production of clinical and academic research tables.
He has presented at major SAS user conferences, including PharmaSUG and WUSS, and has served in conference leadership roles as a section chair. In addition to his biostatistics work, Dr. Akmyradov is active in statistical education, mentoring, and professional service.
Through his teaching, he emphasizes practical examples, step-by-step reasoning, and real-world applications for healthcare, academic, and industry programmers. His goal is to help SAS users move beyond individual programs toward reusable, auditable, and scalable macro-powered systems.
The Living Dead in Your Cohort: Immortal Time Bias Detection and Correction in SAS
Mehrnaz Siavoshi
Immortal time bias is one of the most common and most consequential errors in real world evidence studies, and it almost always makes a treatment look more protective than it truly is. It arises whenever person time between cohort entry and the assignment of exposure is misclassified as exposed, or whenever exposure is defined using information that requires a patient to survive into the future. Because the bias is built into the structure of the analytic dataset rather than into a single modeling option, it cannot be removed by covariate adjustment. This paper provides a practical framework in SAS for recognizing immortal time bias at the design stage and removing it at the analysis stage. Using a fully reproducible simulated cohort in which the true treatment effect is null, we first reproduce the spurious protective hazard ratio that a naive time fixed Cox model returns, and we show how the same bias is already visible in the Kaplan-Meier curves. We then demonstrate three corrections in SAS: a time varying exposure model built with a counting process data layout and with a PROC PHREG programming statement, a landmark analysis, and a target trial emulation using the clone, censor, and weight approach. Each correction recovers the correct null result. The paper closes with a detection checklist so that attendees can audit their own studies.
About the Presenter: Mehrnaz Siavoshi is a biostatistician at UCLA with experience in both academia and industry, currently obtaining her PhD in Epidemiology at Walden University. She specializes in statistical analysis and consulting across various medical fields including urology, surgery, and obstetrics. Her research interests include improving health outcomes, causal methods, and health disparities across the life course.
The Essentials of SAS® Dates and Times
Derek Morgan
The first thing you need to know is that SAS® software stores dates and times as numbers. However, this is not the only thing you need to know. This presentation gives you a solid base for working with dates and times in SAS. It introduces you to functions and features that enable you to manipulate your dates and times with surprising flexibility. This paper shows you some of the possible pitfalls with dates (and times and datetimes) in your SAS code and how to avoid them. We show you how SAS handles dates and times through examples, including the ISO 8601 formats and informats and how to use dates and times in TITLE and FOOTNOTE statements. The paper closes with a brief discussion of Excel conversions.
About the Presenter: Derek is the Associate Director of Global Macro Programming at Bristol Myers Squibb, and has presented many times at SAS User Group conferences. He is perhaps best known as “Mr. Dates and Times” for his authorship of the SAS Press Book, “The Essential Guide to SAS® Dates and Times.” A professional SAS programmer for over 35 years in academia, pharmaceuticals, and other businesses, he spends his spare time playing electric bass in the St. Louis area. His hobby has given him the opportunity to perform onstage with three members of the Rock and Roll Hall of Fame.
The (ODS) Output of Your Desires: a Tool-kit for Creating Designer Reports and Data Sets
Louise Hadden
The Output Delivery System (ODS) delivers what used to be printed output in many convenient forms. What many of us don’t realize is that “printed output” from procedures (whether the destination is PDF, RTF, or HTML) is the result of SAS® packaging a collection of items that come out of a procedure that most people want to see in a predefined order (aka template). This session addresses the opportunity to harness the power of SAS’s Output Delivery System (ODS) and ODS Output Objects to create highly customized reports and data sets tailored to specific needs, saving time and enhancing clarity. Attendees will learn how to trace, manipulate, and repurpose ODS output objects using tools like ODS TRACE, ODS OUTPUT, and SAS reporting procedures to extract precise information, coalesce data, and present it in visually appealing formats across multiple ODS destinations. This session provides tools and concepts to transform procedural output into camera-ready, designer-quality reports.
About the Presenter: Louise Hadden presented at her first SAS conference in 1996 and has never looked back, presenting at multiple conferences across the continent over the years. She supports file building and analytic programming for life sciences organizations, most frequently as a contractor for government agencies such as CMS and CDC, and specializes in reporting and data visualization.
Using SAS® Macro Variable Lists to Create Dynamic Data-Driven Programs
Josh Horstman
Hardcoding data values into your SAS programs creates a fragile infrastructure prone to failure whenever upstream data changes. The solution? Let the macro facility write your code for you. This session explores the power of using macro variable lists to create truly dynamic, data-driven programming logic. We will walk through concrete examples illustrating how to capture live data values, store them in macro arrays, and deploy them to generate adaptive SAS code on the fly. Join us to discover how to banish data dependencies from your environment and transition to an automated, resilient programming workflow.
About the Presenter: Josh Horstman is a statistical programming consultant and trainer based in Indianapolis with over 28 years of experience using SAS. As a partner in PharmaStat LLC, he provides statistical programming support on clinical trials for pharmaceutical clients. Josh is a SAS Certified Advanced Programmer who loves coding and presenting at SAS user group conferences and other industry events. Josh also enjoys travelling and hiking with his family and has been to 48 states and 34 national parks.
SAS Macro Debugging Techniques for Mere Mortals
Kirk Paul Lafler
SAS macro programming is powerful, but when things go wrong, debugging can feel complicated, frustrating, and time-consuming. This Hands-On Training is designed for SAS programmers who understand macro basics but struggle to diagnose and fix macro-related issues efficiently. Using a synthesized Framingham Heart Study dataset (500 observations, 22 variables), participants will learn how to systematically debug macro programs by leveraging SAS system options, diagnostic tools, and structured troubleshooting approaches. The session emphasizes real-world debugging scenarios, including resolving macro variable scope issues, identifying compilation vs. execution errors, handling quoting pitfalls, and interpreting cryptic log messages. Through guided exercises, attendees will gain practical experience using tools such as MPRINT, MLOGIC, SYMBOLGEN, and OPTIONS SOURCE2, as well as techniques for isolating logic errors, validating input parameters, and testing macro output incrementally. By the end of the session, participants will be equipped with a repeatable debugging framework that transforms macro troubleshooting from guesswork into a disciplined, efficient process.
About the Presenter: Kirk Paul Lafler is an internationally recognized Enterprise Data & Systems Architect, Analytics & Data Engineer, Data Scientist, Consultant, and Educator who brings deep, cross-disciplinary expertise spanning analytics, data science, SQL, SAS, database systems, Python, R, Excel, application development, and systems thinking. His work is grounded in the intersection of rigorous technical execution and measurable business outcomes. Recognized for distilling complexity into clear, actionable insight, Kirk equips organizations and professionals with the necessary skills and expertise using hands-on, applied leadership and instruction.
Kirk partners with organizations across industries to improve analytics capabilities, optimize systems, and enable teams to work more efficiently and confidently. As an educator, he is passionate about helping professionals grow their skills, strengthen their problem-solving abilities, and achieve better outcomes through smarter use of data and software. As the author of several books, including PROC SQL: Beyond the Basics Using SAS, Third Edition (SAS Press, 2019), Kirk is a frequent invited speaker, educator, and keynote presenter at conferences and professional events worldwide, and a recipient of 29 “Best” contributed paper, hands-on workshop (HOW), and poster awards.