The heart of WUSS 2025 are the paper presentations and hands-on workshops from SAS users just like you! Papers are organized into 9 academic sections and cover a variety of topics and experience levels.
Note: This information is subject to change. Last updated 26-Aug-2025.
AI, Analytics, and Statistics
| No. | Author(s) | Paper Title (click for abstract) |
| AS-122 | Jayanth Iyengar | Conducting Survival Analysis in SAS using Medicare Claims as a Real-world data source |
| AS-133 | David Horvath | Future trends in AI and impacts on SAS Programming |
| AS-137 | Jesse Canchola & Monica Vasquez & Payam Ahmadi |
Inter-reader Variability and Reader Weighting in Diagnostic Accuracy Studies using the SAS System |
| AS-148 | Charles Silber | Confusion Matrix Applications Across Critical Decision-Making Domains with SAS and R |
| AS-152 | Danny Modlin | Getting Started with SAS Visual Analytics |
| AS-153 | Danny Modlin | PROC BGLIMM: The Smooth Transition to Bayesian Analysis |
| AS-159 | Joe Madden | Get Your Ducks in a Row with SAS Viya Workbench, SAS Data Maker and DuckDB |
| AS-166 | Steve Black | SAS, AI, and Me: Connecting SAS with AI to Become Almost Superhuman |
| AS-174 | Hamed Hemmati & Bennett Frohock |
Evolving Human Emotions Under a Global Crisis: An Analysis of Popular Music Sentiment |
Anything Data
| No. | Author(s) | Paper Title (click for abstract) |
| AD-113 | Troy Hughes | Chasing Master Data Interoperability: Facilitating Master Data Management (MDM) Through CSV Control Tables that Contain Data Rules that Support SAS and Python Data-Driven Software Design |
| AD-134 | David Horvath | A Dynamic Approach to Processing XML Files |
| AD-155 | Jane Eslinger | Making a Readable PROC COMPARE Report in Excel |
| AD-156 | Jane Eslinger | Highlighting the Differences: PROC COMPARE in Excel |
| AD-161 | Yeshashwini Chenna & Lupetu Ntambwe |
PFS2, did you hear about it too? Understanding the derivation of Progression Free Survival on next-line therapy (PFS2). |
| AD-172 | Sonrisa Bebo & Brynn Baker |
A Macroeconomic View of Labor Rights and Fashion Supply Chains |
| AD-173 | Michele Ensor | Microsoft Excel Proficiency with SAS: Unlocking Data Insights and Dynamic Reporting |
| AD-186 | Wendy Christensen | Rerun or Reboot? Reproducibility in a Golden Age of Generative AI |
Basics and Beyond
Careers and Professional Development
| No. | Author(s) | Paper Title (click for abstract) |
| CP-114 | Troy Hughes | Accredited, Bona Fide, Certified, Diploma’ed, and Edumacated: The ABCDEs of Automating the Validation and Monitoring of Professional Requirements for Employees Through Dynamic, Data-Driven SAS Reporting |
| CP-165 | Mehrnoosh Golshahifar | The Role of Congruity and Gendered Leadership Expectations in Higher Education: A Meta-Analysis of Bias and Leadership Evaluations Among Women Leaders |
| CP-171 | Josh Horstman | So You Want To Be An Independent Consultant: 2025 Edition |
| CP-190 | Alyssa Planas | From Newcomer to Networker: Mastering Soft Skills |
Hands-On Workshops (HOWs)
| No. | Author(s) | Paper Title (click for abstract) |
| HW-162 | Charu Shankar | Sugar Rush: SQL Master Class for Pharma Professionals |
Masterpiece Theater Demos
Open Source Solutions
| No. | Author(s) | Paper Title (click for abstract) |
| OS-116 | Jim Box | How did that Python code get in my SAS program? |
| OS-176 | Leon Davoody & Lida Gharibvand |
Car Price Prediction Using Machine Learning Techniques |
| OS-178 | Sunil Gupta | R Made Easier for SAS Programmers |
| OS-203 | Chevell Parker | Next Level Reporting: ODS and Open Source |
| OS-204 | Joe Madden | Friends with Benefits: SAS , SQL, R, and Python in SAS Viya Workbench |
Pharma and Healthcare
Solutions Development
Abstracts
AI, Analytics, and Statistics
AS-122 : Conducting Survival Analysis in SAS using Medicare Claims as a Real-world data source
Jayanth Iyengar, Data Systems Consultants LLC
Friday, 8:30 AM – 8:50 AM, Location: Producers
Applications of Survival analysis as a statistical technique extend to longitudinal studies, and other studies in health research. The SAS/STAT package contains multiple procedures for performing and running survival analysis. The most well-known of these are PROC LIFETEST and PROC PHREG. As a data source, Medicare claims are often used in Real-world evidence studies and observational research. In this paper, survival analysis and the SAS procedures for performing it will be explored, and survival analyses will be conducted using Medicare claims data sets to assess patient’s prognosis amongst Medicare beneficiaries.
AS-133 : Future trends in AI and impacts on SAS Programming
David Horvath, PhilaSUG
Friday, 9:00 AM – 9:20 AM, Location: Producers
AI is a hot topic today that affects how we do our jobs today and in the future. An understanding what we can do now and where we may in the near future. There are things that AI is good at now and where it is way oversold in capability.
AS-137 : Inter-reader Variability and Reader Weighting in Diagnostic Accuracy Studies using the SAS System
Jesse Canchola, Roche Diagnostics Solutions
Monica Vasquez, Roche Diagnostics Solutions
Payam Ahmadi, Roche Diagnostics Solutions
Thursday, 3:00 PM – 3:20 PM, Location: Producers
This paper explores the theoretical foundations, statistical properties, and applied implementation of inter-reader variability and reader weighting techniques in diagnostic accuracy studies using the SAS System. It focuses on scenarios where multiple readers interpret diagnostic data, such as in tissue diagnostics or radiology, and where accuracy metrics such as PPA, NPA, APPA, and ANPA are supplemented with agreement indices like Fleiss’ kappa and intra-class correlation (ICC). The report also provides guidance on how to apply reader and case weighting to better reflect real-world performance.
AS-148 : Confusion Matrix Applications Across Critical Decision-Making Domains with SAS and R
Charles Silber, Educator
Friday, 9:30 AM – 10:20 AM, Location: Producers
Confusion matrices are fundamental evaluation tools for classification models in diverse high-stakes applications, providing performance assessment that goes beyond simple accuracy metrics. This presentation examines three critical implementations that demonstrate the versatility and necessity of confusion matrix analysis. In threat assessment, binary confusion matrices enable the optimization of detection systems by balancing sensitivity and specificity. False negatives signify missed threats with potentially catastrophic results, while false positives lead to operational burdens and inefficient resource allocation. Metrics such as precision and recall enable security teams to calibrate systems according to an organization’s risk tolerance. Medical radiology applications highlight the life-critical nature of classification accuracy. In this field, false negatives may delay essential treatment, while false positives can lead to undue patient anxiety and increased healthcare costs. Confusion matrix analysis allows radiologists to evaluate diagnostic sensitivity for early disease detection while maintaining specificity to minimize unnecessary procedures. The positive predictive value is also crucial for patient counseling and treatment planning. Free response question scoring poses distinct multi-class classification challenges. Traditional binary matrices are expanded, for instance, to 5×5 matrices, to reveal patterns in scoring consistency and systematic bias. This helps identify specific areas where scoring accuracy is low, which allows for targeted training and rubric improvements. Across these domains, confusion matrices provide stakeholders with interpretable metrics that inform actionable business intelligence. The capacity to calculate domain-specific metrics makes confusion matrices indispensable for model validation and optimization.
AS-152 : Getting Started with SAS Visual Analytics
Danny Modlin, SAS
Thursday, 2:00 PM – 2:50 PM, Location: Producers
This workshop shows you how to explore and visualize data with SAS Visual Analytics. You learn how to access and prepare data, how to create interactive charts, reports, and maps to learn about your data, and how to build a preliminary predictive model.
AS-153 : PROC BGLIMM: The Smooth Transition to Bayesian Analysis
Danny Modlin, SAS
Thursday, 5:00 PM – 5:50 PM, Location: Producers
Many analysts are interested in taking models they currently have and transitioning them to the Bayesian realm. Most leap from their favorite classical analysis procedure directly to PROC MCMC, the general-purpose Bayesian procedure. This presentation will feature the BGLIMM procedure available since SAS/STAT 15.1. This will allow the participant to model non-normal responses and include random effects within their Bayesian approach. Discussion will include options of priors and availability of statements. Examples will include models originally written in PROCs REG, GLM, GLMSELECT, GENMOD, MIXED, and GLIMMIX.
AS-159 : Get Your Ducks in a Row with SAS Viya Workbench, SAS Data Maker and DuckDB
Joe Madden, SAS
Thursday, 4:00 PM – 4:50 PM, Location: Producers
Are long-running jobs eating into your productivity? SAS Viya Workbench and SAS Data Maker are here to give you more time back in your day. SAS Viya Workbench is a developer-focused platform designed to empower data scientists who need to start small but scale efficiently. Whether you code in Python, R, or SAS, Viya Workbench provides a flexible, cloud-native environment that accelerates your workflow without compromising control or performance. This session will cover how Workbench and Data Maker can enable a developer to rapidly build and prototype against your existing data sources with very little setup required. We will show you how we are using DuckDB across our newest tools and highlight the 5x+ performance gains we are seeing when compared against traditional SAS techniques.
AS-166 : SAS, AI, and Me: Connecting SAS with AI to Become Almost Superhuman
Steve Black, Neurocrine Biosciences
Friday, 10:30 AM – 10:50 AM, Location: Producers
As AI technology increasingly shapes our professional and personal worlds, mastering its integration with platforms like SAS is essential. In this paper, I will explore how to harness the combined power of SAS and AI technologies to elevate our capabilities. By utilizing an API Key, I’ll demonstrate how to access AI models directly through SAS, enabling the transfer of data and code to gain profound AI-driven insights. Furthermore, I’ll highlight several real-world applications of AI that I utilize in my work, extending beyond the SAS environment. Join me in discovering how the fusion of SAS and AI can enhance our abilities to almost superhuman levels, driving remarkable productivity and creativity.
AS-174 : Evolving Human Emotions Under a Global Crisis: An Analysis of Popular Music Sentiment
Hamed Hemmati, Oklahoma State University
Bennett Frohock, Oklahoma State University
Friday, 11:00 AM – 11:20 AM, Location: Producers
This study explores the profound impact of the COVID-19 pandemic on popular music lyrics, revealing shifts in thematic and emotional content across Pre-COVID (2018 2019), During COVID (2020 2021), and Post-COVID (2022 2024) periods. By mining over 1,200 top songs from Spotify and Musixmatch and analyzing their lyrics using advanced AI tools (GPT-4o-mini) and statistical methods, including K-means clustering and chi-square tests, we uncover how global crises shape artistic expression. During the pandemic, lyrics emphasized introspective themes education, work, and community paired with darker tones of anxiety and depression, reflecting societal uncertainty. Post-COVID, aspirational narratives of wealth, success, and confidence emerged, signaling collective renewal. Core themes like love and heartbreak remained timeless, while bivariate analyses highlight dynamic pairings, such as resilience with hopefulness during COVID. This research underscores music’s role as a cultural barometer and coping mechanism, offering data-driven insights for SAS users in analytics, natural language processing, sociology, and behavioral studies. No specific system or software requirements are needed, as the presentation focuses on methodology and findings, accessible to all data professionals.
Anything Data
AD-113 : Chasing Master Data Interoperability: Facilitating Master Data Management (MDM) Through CSV Control Tables that Contain Data Rules that Support SAS and Python Data-Driven Software Design
Troy Hughes, Data Llama Analytics
Thursday, 10:00 AM – 10:20 AM, Location: Producers
Control tables are the tabular data structures that contain control data the data that direct software execution and which can prescribe dynamic software functionality. Control tables offer a preferred alternative to hardcoded conditional logic statements, which require code customization to modify. Thus, control tables can dramatically improve software maintainability and configurability by empowering developers and, in some cases, nontechnical end users to alter software functionality without modifying code. Moreover, when control tables are maintained within canonical data structures such as comma-separated values (CSV) files, they facilitate master data interoperability by enabling one control table to drive not only SAS software but also non-SAS applications. This text introduces a reusable method that preloads CSV control tables into SAS temporary arrays to facilitate the evaluation of business rules and other data rules within SAS data sets. To demonstrate the interoperability of canonical data structures, including CSV control tables, a functionally equivalent Python program also ingests these control tables. Master data management (MDM) objectives are facilitated because only one instance of the master data the control table, and single source of the truth is maintained, yet it can drive limitless processes across varied applications and programming languages. Finally, when data rules must be modified, the control data within the control table can be changed only once to effect corresponding changes in all derivative uses of those master data.
AD-134 : A Dynamic Approach to Processing XML Files
David Horvath, PhilaSUG
Thursday, 10:30 AM – 10:50 AM, Location: Producers
The XML engine within SAS is very powerful but it does convert every object into a SAS dataset with generated keys to implement the parent/child relationships between these objects. Those keys (Ordinals in SAS-speak) are guaranteed to be unique within a specific XML file. However, they restart at 1 with each file. When concatenating the individual tables together, those keys are no longer unique. We received an XML file with over 110 objects resulting in over 110 SAS datasets our internal customer wanted concatenated for multiple days. Rather than copying and pasting the code to handle this process 110+ times, and knowing that I would make mistakes along the way and knowing that the objects would also change along the way, I created SAS code to create the SAS code to handle the XML. I consider myself a Lazy Programmer. As the classic “Real Programmers” sheet tells us, Real Programmers are Lazy. This session reviews XML, SAS XML Mapper, SAS XML Engine, techniques for handing the Ordinals over multiple days, and finally discusses a technique for using SAS code to generate SAS code.
AD-155 : Making a Readable PROC COMPARE Report in Excel
Jane Eslinger, Eslinger Enterprises
Thursday, 11:00 AM – 11:50 AM, Location: Producers
When using PROC COMPARE to examine two data sets for differences, the default output is a verbose and segmented report optimized for the ODS Listing destination. When sent to Excel, the report, with one variable holding all the information, becomes hard to comprehend. This paper demonstrates how to capture and manipulate key ODS OUTPUT data sets generated by PROC COMPARE. Then using those data sets with PROC REPORT to create a cleaner, more readable report in Excel perfect for review, documentation, or delivery.
AD-156 : Highlighting the Differences: PROC COMPARE in Excel
Jane Eslinger, Eslinger Enterprises
Friday, 9:30 AM – 10:20 AM, Location: East Ballroom
PROC COMPARE is great for examining the differences across two data sets but the default output doesn’t always paint the full picture. Though the printed report is informational, its wordy presentation does not facilitate the identification of patterns in the differences within and across variables. This paper shows how to turn raw comparison data from the OUT= options in PROC COMPARE into a polished spreadsheet that uses color strategically to highlight the details and make variable-level differences easy for the reviewer to spot.
AD-161 : PFS2, did you hear about it too? Understanding the derivation of Progression Free Survival on next-line therapy (PFS2).
Yeshashwini Chenna, Amgen inc
Lupetu Ntambwe, Amgen Inc
Thursday, 5:30 PM – 5:50 PM, Location: Writers
In Clinical Trials, Endpoints are important milestones for determining how effective a treatment is. One such is the Progression Free Survival (PFS) endpoint. PFS is defined as the time from the start of the treatment (or randomization) until the progression of disease or death from any cause, whichever event comes first. In some clinical trials, a next-line therapy is often used as a follow-up treatment, depending on the condition being treated. PFS on the next-line therapy or PFS-2 is defined as the time from randomization to either progression on the next line of treatment or death from any cause. Patients who are alive and for whom a next line progression is not observed should be censored to the last known alive date and without next line progression.
AD-172 : A Macroeconomic View of Labor Rights and Fashion Supply Chains
Sonrisa Bebo, Oklahoma State University
Brynn Baker, Oklahoma State University
Friday, 11:00 AM – 11:20 AM, Location: East Ballroom
The global apparel industry plays a vital role in the world economy, yet it remains closely tied to labor rights violations. This study examines the economic conditions that contribute to exploitative labor practices in apparel-producing countries, addressing a critical gap in understanding the relationship between macroeconomic factors and labor violations. Using data from the International Labour Organization (ILO) and the World Bank, this research analyzes key economic indicators, including apparel trade, workplace injuries, forced labor, working time, GDP (gross domestic product) per capita, average monthly wages, unemployment rates, and trade openness. To explore these relationships, the study uses SAS Viya to apply Gradient Boosting modeling to assess the likelihood of labor rights violations based on these variables and identify relationships in the dataset. Findings suggest that lower-income countries with weak labor protections and high trade dependence are more susceptible to exploitative labor conditions. These results indicate that economic instability and weak regulatory frameworks create environments where labor violations are more likely to occur. The study’s findings highlight the need for stronger labor protections and ethical sourcing practices, offering valuable insights for policymakers, businesses, and advocacy organizations. By incorporating predictive analytics, this research provides a data-driven perspective on labor rights challenges and offers recommendations for building more sustainable and ethical supply chains.
AD-173 : Microsoft Excel Proficiency with SAS: Unlocking Data Insights and Dynamic Reporting
Michele Ensor, SAS
Friday, 8:30 AM – 9:20 AM, Location: East Ballroom
In this demonstration, discover the art of seamlessly reading and writing Microsoft Excel data with SAS, unlocking a realm of possibilities. From Excel data ingestion to crafting dynamic reports in Excel using SAS, this session empowers you to navigate the convergence of SAS and Excel. Learn efficient SAS techniques to harness the power of both tools, ensuring a seamless workflow for comprehensive data analysis and reporting. Elevate your skills and gain the expertise needed to unleash the full potential of SAS in creating impactful Excel reports.
AD-186 : Rerun or Reboot? Reproducibility in a Golden Age of Generative AI
Wendy Christensen, University of Colorado School of Medicine
Friday, 10:30 AM – 10:50 AM, Location: East Ballroom
Reproducibility, which is the ability to rerun an analysis and obtain the same results, is a cornerstone of trustworthy research and other products of data analysis. As we enter a golden age of generative AI, however, traditional tools and frameworks for reproducibility can be difficult to apply when using generative AI tools. In this presentation, which assumes no familiarity with reproducibility, I will discuss the foundations of reproducibility and provide an overview of tools and procedures used in traditional analytic workflows to enhance reproducibility. Then, I will discuss the challenges to reproducibility that generative AI tools present and offer strategies for adapting existing ideas and tools to this rapidly evolving landscape.
Basics and Beyond
BB-106 : Going to great lengths: Using VARCHARs in the DATA step
Rick Langston, retired
Thursday, 10:00 AM – 10:50 AM, Location: Directors
This paper describes the use of VARCHARs within the DATA step. This feature was added in SAS 9.4m5 and may not be widely known. A VARCHAR allows for a varying length of data to be stored, as compared to a fixed length for character variables. Also, a VARCHAR allows for a much larger character storage than character variables. Many examples of VARCHAR usage are given and explained.
BB-108 : The (ODS) Output of Your Desires: Creating Designer Reports and Data Sets
Louise Hadden, Cormac Corporation
Wednesday, 3:00 PM – 3:50 PM, Location: Directors
SAS procedures can convey an enormous amount of information sometimes more information than is needed. Most SAS procedures generate ODS objects behind the scenes. SAS uses these objects with style templates that have custom buckets for certain types of output to produce the output that we see in all destinations (including the SAS listing). By tracing output objects and ODS templates using ODS TRACE (DOM) and by manipulating procedural output and ODS OUTPUT objects, we can pick and choose just the information that we want to see. We can then harness the power of SAS data management and reporting procedures to coalesce the information collected and present the information accurately and attractively.
BB-115 : Badge in Batch with Honeybadger: Generating Conference Badges with Quick Response (QR) Codes Containing Virtual Contact Cards (vCards) for Automatic Smart Phone Contact List Upload
Troy Hughes, Data Llama Analytics
Wednesday, 2:30 PM – 2:50 PM, Location: Directors
Quick Response (QR) codes are widely used to encode information such as uniform record locators (URLs) for websites, flight passenger data on airline tickets, attendee data on concert tickets, or product information on product packaging. The proliferation of QR codes is due in part to the broad dissemination of smart phones and the accessibility of free smart phone applications that scan QR codes. With the ease of QR code scanning has come another common QR code usage the identification of conference attendees. Conference badges, emblazoned with attendee-specific QR codes, can communicate attendee contact information to other conference goers, including organizers, vendors, potential customers or employers, and others. Conference badges that contain QR codes make it easy for attendees to link up with each other because snapping a photo of a badge can immediately capture contact information (that could not otherwise be printed on the badge itself), thus eliminating the need to exchange outmoded business cards. To that end, this text introduces flexible Base SAS software that dynamically creates attendee QR codes from a data set containing contact and other information. This data-driven approach could be used to create attendee badges by conference organizers rather than costly third-party vendors. When a badge QR code is scanned at a conference, the attendee’s personal information which can include name, job title, company, phone number, email address, city, state, website, and brief biographical statement is ported into a variant call format (VCF) file (or vCard) that can be uploaded automatically into a smart phone’s contact list. Attendees are able to select what personal information is contained within their QR code and conference organizers are able to customize and configure badge format and content through data-driven software design an external cascading style sheets (CSS) file that dynamically alters badges without the necessity to modify the underlying code.
BB-120 : Applications of PROC COMPARE to Parallel Programming and other projects
Jayanth Iyengar, Data Systems Consultants LLC
Wednesday, 4:00 PM – 4:20 PM, Location: Directors
PROC COMPARE is a valuable BASE SAS procedure which is used heavily in the Pharma industry and other areas. By default, the capability of PROC COMPARE is to reconcile two data sets to determine if they have equivalent sets of records and sets of variables. In the clinical field and elsewhere, PROC COMPARE is often used to validate data sets in projects which involve parallel programming, where programmers independently perform the same tasks. In this paper, I will discuss the role PROC COMPARE plays in different SAS tasks, including DATA STEP merges, parallel programming, generation data sets, and more.
BB-121 : Validate the Code, not just the Data : A System for SAS program validation
Jayanth Iyengar, Data Systems Consultants LLC
Wednesday, 4:30 PM – 4:50 PM, Location: Directors
Regardless of the industry they work in, SAS programmers are focused on validating data, and devote a considerable amount of attention to the quality of data, whether its raw source data, submitted SAS data sets, or SAS output, including figures and listings. No less important is the validity of code and the SAS programs which extract, manipulate, and analyze data. Although code validity can be assessed through the SAS log, there other ways to produce metrics on code validity. This paper introduces a system for SAS program validation which produces useful information on lines of code, number of data steps, total run and CPU time and other metrics for project-related SAS programs.
BB-123 : You’ve Got Options: Five-Star SAS System Option Hacks
Louise Hadden, Cormac Corporation
Wednesday, 5:00 PM – 5:20 PM, Location: Directors
SAS provides myriad opportunities for customizing programs and processes, including a wide variety of system options that can control and enhance SAS code from start to finish. This paper and presentation demonstrates methods of obtaining information on SAS system options, and moves on to fully explicate ten SAS system option hacks, from COMPRESS to VALIDVARFMT. System options are highly dependent on platforms, security concerns, SAS versions and products: dependencies and defaults will be discussed. SAS practitioners will gain a deeper understanding of the powerful SAS system options they’ve seen, used, and automatically included in their code. This presentation is suitable for all skill and experience levels; platform differences are part of the discussion.
BB-125 : ExCITE-ing! Build Your Paper’s Reference Section Programmatically Using Lex Jansen’s Website and SAS
Louise Hadden, Cormac Corporation
Wednesday, 5:30 PM – 5:50 PM, Location: Directors
One challenge in writing a SAS White Paper is creating the perfect reference section, properly acknowledging those who have inspired and paved the way. Luckily, clever use of such tools as Lex Jansen’s website, SAS’s ability to read in and manipulate varied data sources, and Microsoft Word citation manager, every author can succeed in proper referencing in their white papers. This paper and presentation will demonstrate how to accomplish this goal.
BB-126 : The World is Not Enough: Base SAS Visualizations and Geolocations
Louise Hadden, Cormac Corporation
Thursday, 8:30 AM – 8:50 AM, Location: Directors
Geographic processing in SAS has recently undergone some major changes: as of Version 9.4 Maintenance Release M5 many procedures formerly a part of SAS/Graph are now available in BASE SAS. At the same time, SAS Graphics have added some new procedures such as PROC SGMAP that build on the functionality of SAS/GRAPH’s PROC GMAP and incorporate ODS graphics techniques including attribute maps and image annotation. This paper and poster will replicate a map of the world created by the author with SAS/GRAPH and PROC GMAP with the annotate facility using PROC SGMAP to map three different metrics on a map of the world. New SAS mapping and SG procedure techniques will be demonstrated, following Agent 007’s adventures across the globe.
BB-143 : Design and Development of an ASIC-Based Convolutional Neural Network Using Open-Source Tools
Riya Soni, MONK9 TECHNOLOGY
Thursday, 9:00 AM – 9:20 AM, Location: Directors
This work describes the design and implementation of a Convolutional Neural Network (CNN) accelerator in Verilog for ASIC design targeting real-time image recognition applications. The design is synthesized and implemented with open-source tools such as OpenLane, Sky130 PDK, and incorporated into a RISC-V SoC based on the PicoRV32 core. Once the RTL-to-GDSII process is finished, the chip is incorporated into the Efabless Caravel harness, supporting simple SoC incorporation, testing, and chip-level communication. Upon fabrication, the Caravel board enables external users to provide input images to the chip, which are executed by the CNN logic on silicon, exhibiting real-time inference on hardware. This work shows an all-open-source methodology to the development of AI-capable SoCs, illustrating how open tools and platforms can enable advanced chip design for edge intelligence and academic research.
BB-191 : SAS Essentials I: How SAS Thinks
Susan Slaughter, Avocet Solutions
Wednesday, 2:30 PM – 3:20 PM, Location: Producers
This presentation introduces SAS programming at the most basic level. Topics include the structure of SAS data sets, DATA versus PROC steps, the DATA step’s built-in loop, character versus numeric data, SAS date values, and the various ways to run SAS programs and get data into SAS. By the end of this presentation you will have a clear idea of what SAS is and how you can use it.
BB-192 : SAS Essentials II: Introduction to DATA Step Programming
Susan Slaughter, Avocet Solutions
Wednesday, 3:30 PM – 4:20 PM, Location: Producers
The DATA step is the workhorse of SAS. It has the flexibility of a regular programming language but with SAS’s own unique built-in structure. Topics include assignment statements, functions, conditional logic, and subsetting and combining SAS data sets. This will be a quick overview of the endless ways you can use DATA steps to manipulate your data.
BB-193 : SAS Essentials III: Introduction to SAS Procedures
Susan Slaughter, Avocet Solutions
Wednesday, 4:30 PM – 5:20 PM, Location: Producers
SAS procedures are the powerhouses of SAS. They are subroutines you can use to perform tasks from the simple to the extremely complex. This presentation will focus on sorting and reporting. Procedures covered include CONTENTS, SORT, PRINT, FREQ, MEANS, and SQL. It’s important for your output to look good and be easy to understand. So we’ll also cover TITLE, LABEL, and FORMAT statements, and how to use the Output Delivery System to choose your file format and overall style.
BB-195 : Enhance your Coding Experience with the SAS Extension for VS Code
Jim Box, SAS Institute
Thursday, 9:30 AM – 9:50 AM, Location: Directors
Visual Studio Code (VS Code) from Microsoft is an open-source code editor that is very popular among developers for its ease of use across all programming languages which is driven by a robust extension ecosystem. The SAS VS Code extension is an open-source, freely available add-on that allows you to use VS Code to connect to any modern SAS Environment, from SAS 9.4 on your local machine to SAS Viya in the cloud. The key features include Syntax Highlighting, Code Completion, Syntax Help, Data Viewer, and my favorite, SAS Notebooks, which offer an exciting way to share content and comments. We’ll look at the extension, how to use it, and explore how you can get involved with the direction of how this product evolves.
Careers and Professional Development
CP-114 : Accredited, Bona Fide, Certified, Diploma’ed, and Edumacated: The ABCDEs of Automating the Validation and Monitoring of Professional Requirements for Employees Through Dynamic, Data-Driven SAS Reporting
Troy Hughes, Data Llama Analytics
Thursday, 2:00 PM – 2:20 PM, Location: Directors
Job postings typically have stated requirements such as education, training, certifications, and other criteria. Some requirements must be met before a candidate is hired while others can be fulfilled after employment during a “grace period” of a specified duration. Because many certifications require periodic refresh or renewal, validation of professional requirements often continues after candidates are hired and throughout their careers. While validation and monitoring professional requirements for a small team might be accomplished in minutes, the effort increases with the number of employees, the diversity of employee roles, and the diversity of requirements that must be tracked. This text demonstrates a flexible, scalable, reusable macro (COMPLIANCE) that validates and monitors education, training, and professional certifications for job candidates or employees. The data-driven SAS solution relies on an external data model (operationalized in an Excel spreadsheet) that specifies the requirements, desired achievements, and applicable grace periods. This software modularity the separation of the data model from the underlying SAS software ensures that the solution can be adapted to any industry, environment, or organization by modifying only the spreadsheet. COMPLIANCE creates dynamic, color-coded HTML reports that delineate personnel who meet requirements, who are within a grace period, who do not currently meet requirements but have met requirements in the past, and who have never met requirements, enabling immediate visual identification of top performers or non-compliant employees.
CP-165 : The Role of Congruity and Gendered Leadership Expectations in Higher Education: A Meta-Analysis of Bias and Leadership Evaluations Among Women Leaders
Mehrnoosh Golshahifar, Doctoral Candidate
Thursday, 3:00 PM – 3:20 PM, Location: Directors
The Role of Congruity and Gendered Leadership Expectations in Higher Education: A Meta-Analysis of Bias and Leadership Evaluations Among Women Leaders This meta-analysis study examines the evaluations of gendered leadership within higher education institutions, employing role congruity theory as its framework for analysis. Eagly and Karau (2002) explain that prejudice against female leaders emerges because people view leadership positions as incompatible with traditional feminine characteristics. The analysis combines data from six studies conducted between 2022 and 2024, which use quantitative metrics, including group means and standard deviations, as well as t-tests and effect sizes (Cohen’s d, Hedges’ g), to examine how gendered expectations shape leadership competence and style, as well as perceptions of emergence. This meta-analysis’s central research question examines the extent to which role congruity bias influences the evaluation of women leaders in higher education institutions. Studies have shown that female leaders got lower scores for efficiency and leader emergence, even though their performance was the same as or better than that of male leaders. The effect sizes demonstrated both small and medium levels of bias, which confirms the theory that female leaders encounter biased evaluations when their actions deviate from societal gender norms. This meta-analysis study demonstrates that educational institutions need to review their policies because educational organizations maintain a vital structure that affects society through their leadership hiring, training, and evaluation systems, which promote gender equality. Keywords: role congruity theory, gender bias, women in leadership, higher education, meta-analysis, transformational leadership, Cohen’s d, Hedges’ g.
CP-171 : So You Want To Be An Independent Consultant: 2025 Edition
Josh Horstman, PharmaStat LLC
Thursday, 11:00 AM – 11:50 AM, Location: Directors
While many statisticians and programmers are content with a traditional employment setting, others yearn for the freedom and flexibility that come with being an independent consultant. While this can be a tremendous benefit, there are many details to consider. This paper will provide an overview of consulting as a statistician or programmer. We’ll discuss the advantages and disadvantages of consulting, getting started, finding work, operating your business, and various legal, financial, and logistical issues. The paper has been recently updated to reflect the new realities of independent consulting in 2025 and beyond.
CP-190 : From Newcomer to Networker: Mastering Soft Skills
Alyssa Planas, Office of Analytics State of Nevada
Thursday, 2:30 PM – 2:50 PM, Location: Directors
As a new professional navigating the data analytics field, attending conferences like WUSS and PharmaSUG has been instrumental in my career growth. These events offer far more than technical presentations they provide invaluable opportunities to connect, learn, and develop professionally. In this presentation, I will share practical strategies on how to effectively network and engage at SAS conferences to maximize your return on investment. Attendees will learn how to identify key sessions, approach and build relationships with industry peers and experts, and leverage conference resources to enhance career development. By highlighting my personal experiences and lessons learned, this session aims to empower new professionals to make the most of these gatherings and advance their careers in the SAS ecosystem.
Hands-On Workshops (HOWs)
HW-162 : Sugar Rush: SQL Master Class for Pharma Professionals
Charu Shankar, SAS Institute
Thursday, 8:30 AM – 9:50 AM, Location: Producers
Join Charu Shankar, a seasoned SAS presenter, for a dynamic presenttion exploring SQL’s sweet role in pharmaceutical data analysis, with a focus on diabetes clinical trials. This session tackles industry-specific challenges using sugar-rich datasets like DCCT, NHANES, and TDS, offering hands-on practice with real-world glucose data. Participants will learn to query, summarize, and report clinical data, including HbA1c levels, demographics, and treatment outcomes. Key skills covered: Data Exploration: Sifting through the sugar to assess diabetes progression. Data Preparation: Blending datasets into a smooth analytical base. Advanced SQL: Sweetening your queries with averages, correlations, and metrics. Data Reporting: Serving up clear insights on treatment outcomes. By the end, attendees will confidently analyze clinical trial data, uncover trends in sweetness, and drive data-driven decisions in diabetes research.
Masterpiece Theater Demos
MT-118 : Responsible use of Artificial Intelligence
Jim Box, SAS Institute
Wednesday, 3:30 PM – 3:50 PM, Location: West Ballroom
Everybody is talking about AI and how to use it, but not enough people understand or are considering the potential problems that using AI can bring to society. Let’s talk about how AI systems are made, the types of problems they are being used to solve, and how to be more responsible in how we use it. We’ll talk about the sorts of questions you should be thinking about and asking any time you are interacting with an AI powered system
MT-119 : Managing Multiple R environments in SAS
Jim Box, SAS Institute
Thursday, 3:00 PM – 3:20 PM, Location: West Ballroom
You probably know that you can submit R code in SAS programs via PROC IML, but did you know you could have multiple R environments to choose from? I’ll show you some SAS code that allows you to switch between R environments, and how to create a listing of all of the packages and their versions in each environment.,
MT-131 : Shining a spotlight on PRELOADFMT option
Sunija Mammen, Amgen Inc.
Friday, 8:30 AM – 8:50 AM, Location: West Ballroom
How can we show the elusive unicorns in the database? Our data may only actually show dogs and cats, but management may want to get a sense of “unicorns” once they appear. Unicorn as poster references the idea is just another way to express missing data which is not available but may appear later. Our report has to keep a place holder for data when / if it should appear. PRELOADFMT is one such way to present data that is currently not available but is bound to show up when least expected. And while there are many ways to keep track of what is present versus what is theoretically possible the poster shows yet another method.
MT-136 : FDA says, Do the Allowable Total Difference Zone: A Method Comparison using the ATDzone SAS Macro
Jesse Canchola, Roche Diagnostics Solutions
Natasha Oza, Roche Diagnostics Solution
Thursday, 9:00 AM – 9:20 AM, Location: West Ballroom
When comparing two systems using the same item or “sample” to produce at least two “paired” results (one on each system), for example, a new versus an older system for a molecular assay, typical method comparison methodologies used are Ordinary Least Squares (OLS), Deming, or Passing-Bablok regression (Passing & Bablok, 1983), bias plots that include Bland-Altman and Krouwer bias plot analysis and Error Grid Analysis (EGA) (Passing & Bablok, 1983; Linnet, 1993; Altman & Bland, 1983; Bland & Altman, 1986; Clark et al., 1987; Parkes et al., 2000). One additional enhancement to most of these methods, not typically used for this type of analysis, called Allowable Total Difference (ATD) zone (CLSI EP21-A, 2003; Krouwer & Monti, 1995; Krouwer, 2008), utilizes the reproducibility results of the older system (for example, from package/product inserts or from product requirements document) to construct the boundaries or limits that define where 95% of the differences between the two repeated measurements by the older system should be inside of those limits in the context of a Bland-Altman or Krouwer bias plot. One application of the ATD Zone include using it in a bias scatterplot of a new system (Y) compared to an older system (X). Producing ATD Zone plots can be a challenging programming endeavor. However, the authors a introduce a SAS macro, ATDzone, that simplifies their creation for any method comparison task at hand, using the Bland-Altman bias plots, with minimal inputs.
MT-141 : SAS Viya for SAS 9.4 Users: What You Need to Know to Work Smarter, Code Faster, and Scale Bigger
Melodie Rush, SAS
Thursday, 2:00 PM – 2:50 PM, Location: West Ballroom
Moving from SAS 9.4 to SAS Viya doesn’t mean starting over it means unlocking new capabilities while building on what you already know. In this session, we’ll translate familiar SAS 9.4 skills into the modern Viya platform, highlighting key differences in architecture, processing, and programming. You’ll learn how to migrate your code, build and deploy machine learning models faster, and take advantage of powerful open-source integration. Whether you’re a programmer, analyst, or architect, this session will help you confidently embrace SAS Viya and get more done with less code, better performance, and smarter tools. Think of this as the session for everything you wanted to know about SAS Viya… but didn’t know how to ask.
MT-147 : Supercharge Your SAS Workflow with Git Integration
Chris Hemedinger, SAS
Friday, 9:00 AM – 9:50 AM, Location: West Ballroom
Ready to level up your SAS Studio experience? In this session, you’ll learn how to connect your Git or GitHub profile to SAS Studio using HTTPS authentication. We’ll walk through cloning a remote repository, managing files in your local SAS Studio repo, and making seamless updates. You’ll modify code, commit changes, push updates back to GitHub, and see how everything syncs beautifully between platforms. Whether you’re collaborating with a team or managing your own projects, this workflow will help you stay organized, efficient, and in control.
MT-149 : Charting Your AI Journey: A Roadmap for Supervised, Unsupervised, and Generative Learning through Machine Learning and Deep Learning
Ryan Lafler, Premier Analytics Consulting, LLC
Miguel Bravo Martinez del Valle, Premier Analytics LLC
Wednesday, 4:00 PM – 4:50 PM, Location: West Ballroom
Machine learning (ML) continues to reshape business, technology, science, and research across all industries, with its adoption enabling systems to learn from data, automate decisions, and generate insights. This paper presents a structured roadmap through three core domains of machine learning that are increasingly adopted by organizations: supervised, unsupervised, and generative learning. Along this roadmap, readers will identify key algorithms and architectures within each domain and understand the role of parameters and hyperparameters in mitigating overfitting and underfitting. The discussion includes examples of predictive modeling on labeled data using supervised algorithms, knowledge discovery from unlabeled data using unsupervised algorithms, and the extension of these capabilities through generative learning, which enables systems to extract insights and produce new content or data representations. The paper concludes by introducing three generative architectures that define the state of AI in 2025: encoder models (BERT), decoder models (LLMs), and encoder-decoder models (T5), and describes how each supports advanced AI tasks including representation learning, language generation, natural language processing (NLP), text summarization, and translation.
MT-150 : A Python Roadmap for Accessing & Leveraging Big Environmental Data Repositories in the Cloud
Ryan Lafler, Premier Analytics Consulting, LLC
Thursday, 4:00 PM – 4:50 PM, Location: West Ballroom
The democratization, growth, and widespread adoption of the Python programming language and its open-source libraries are empowering professionals, researchers, educators, and students to work directly with large environmental data repositories hosted in the cloud. Paired with Open Data initiatives supported by major cloud providers like Amazon, Microsoft, and Google, this shift is making environmental data more accessible than ever, with applications across industries that are more useful than ever. This demo highlights how environmental data can support a wide range of industries and introduces the main file types and data structures commonly used in these datasets. It walks through how any user can initialize a Python connection with a cloud object storage provider, build a data retrieval pipeline, and lazily load terabytes of environmental data directly from Amazon S3, Google Cloud Storage (GCS), and Microsoft Azure into a Python session using libraries including Dask, fsspec, and Xarray.
MT-160 : Monetize Your SAS Insights
Karen Walker, Walker Consulting LLC
Friday, 10:00 AM – 10:50 AM, Location: West Ballroom
By combining compelling visual storytelling with open research practices, this Masterpiece Theater Demo offers a scalable and ethical path for creators to generate revenue while contributing to the broader knowledge WUSS ecosystem. System requirements: Minimum 16GB RAM, multi-core CPU, and GPU acceleration for video rendering. Software requirements: monetization channels YouTube Partner Program, Patreon, digital downloads, and licensing for educational or commercial use. Documentation: GitHub Pages, and static site generators. Coding and Data Managment: SAS Studio, GitHub for version control, collaboration, and public access to Jupyter Notebooks, Python scripts, and structured datasets. Video Editing: CAPCUT, or open-source alternatives like Shotcut for high-quality video creation. Limitations: Reliance on third-party platforms (YouTube, GitHub) introduces risks related to policy changes or service availability. This paper explores a hybrid model for monetizing documentary-style video content while preserving the transparency, reproducibility, and collaborative potential of open-source research. The proposed framework integrates video storytelling with the structured publication of source code, datasets, and analytical insights on GitHub. This approach enables creators to engage both general audiences and technical communities, fostering trust and extending the impact of their work. We will use a PK/PD food study with insights generated with SAS, Data Tables, and SAS MACROS all placed in GitHub to create a documentary video of the presentation with script, voice over, music and animated images to prove our inferences about food effects on a drug. Then upload the video to YouTube and monetize the video’s impact and interest: views, ads, hours watched, and subscribers. Monetize your SAS Insights has been Uploaded and will Screen in the Masterpiece Theater Demo.
MT-177 : ShellTOC: Real-Time Shell & TOC Automation for Fast, Discrepancy-Free Clinical Outputs
Jeff Cao, RealtimeCRO Inc
Thursday, 5:00 PM – 5:50 PM, Location: West Ballroom
50-Minute Masterpiece Theater Demo Abstract: Manual mock-shell and table-of-content (TOC) management slows every biostatistics team and is error prone. ShellTOC replaces that tedium with real-time, standards-compliant automation. It ensures the output title, subtitle, and footnotes exactly match bi-ostatistician’s shell design via real-time synchronization. Shell annotations aid the auto generation of SAS/R programs. This 50-minute live demo walks attendees through a typical analysis workflow: Creation of a draft Shell documents in minutes Quick edit of Title Footnotes information Quick cross-table row handling Quick cross-table column updating Shell annotation Shell/TOC synchronization Audit trail and version history Subsequent auto-generation of SAS/R programs Output Compare automation Potential integration with other systems. Attendees will leave knowing how ShellTOC: Cuts shell/TOC preparation time by >70 % Removes copy-paste errors, shell-output discrepancies, and version drift entirely Supercharges biostatistician programmer collaboration through real-time synchro-nization
MT-188 : Building Better Data Science Workflows: Core Practices with Git, GitHub, and Data Version Control (DVC) for Effective Collaboration
Ryan Lafler, Premier Analytics Consulting, LLC
Miguel Bravo Martinez del Valle, Premier Analytics LLC
Friday, 8:30 AM – 8:50 AM, Location: West Ballroom
Supercharge your data science workflow with Git, GitHub, and Data Version Control (DVC)! This practical session dives into essential version control tools every data team should master featuring hands-on tips, real-world examples, and integration strategies to efficiently track changes to both code and data. Discover how Git enables clean branching, purposeful commits, and streamlined collaboration. Push those local commits to GitHub to unlock team-based workflows with pull requests, protected branches, and remote repository management. DVC then extends Git by tracking large datasets and machine learning (ML) models stored in local systems or external servers and cloud storage providers without bloating your Git repository. From making meaningful and informative commits to safely stashing changes and managing parallel branches, this session delivers actionable tips, tricks, and techniques to help your team version smarter, work in parallel, reduce merge conflicts, and collaborate more effectively across the stack.
MT-196 : The GENMOD Toolkit: What do you do when things aren’t Normal?
Danny Modlin, SAS
Thursday, 9:30 AM – 9:50 AM, Location: West Ballroom
Life is nice in the statistical world when things are normal (distributed that is). We have performed multitudes of regression analyses where the residuals meet the required assumptions of normality. What happens when this normality is not present? What do you do when your response variable is a count or a rate? Welcome to the world of generalized linear models! In this demonstration, we will explore this expansion from normality using the GENMOD procedure in SAS. Suggestions for deciding which distribution to use will be shared. Our discussion and examples will also include a look into zero-inflation and one of the more interesting distributions, the Tweedie distribution.
MT-197 : Large-Scale Time Series Forecasting in Model Studio
Danny Modlin, SAS
Thursday, 10:00 AM – 10:20 AM, Location: West Ballroom
In this demonstration, you learn to build time series models for large-scale time series problems with many hierarchically related series. You will experience the capability of Model Studio to diagnose, fit, and assess models for many time series at once. Use the new Hierarchical Modeling Node to create time series models at each of the levels of the hierarchy. Need to extract your reconciled predictions from each level of the hierarchy? No problem. Within the Hierarchical Modeling Node, you can dive into each level of the hierarchy and export these desired predictions.
MT-198 : Introduction to SAS Viya
Jim Box, SAS Institute
Thursday, 8:30 AM – 8:50 AM, Location: West Ballroom
Heard about SAS Viya but are not sure what that means (or even how to pronounce it)? Join us for an overview where we’ll show you what it’s all about. You’ll see: Basic Architecture Description New ways to do SAS coding, both with new PROCS and with new tools New ways to connect to Data Sources Automated Dataset explorations Interactive Dashboards and Analyses Model Building
MT-199 : The Program Data Vector and Data Step Debugger
Charu Shankar, SAS Institute
Thursday, 11:30 AM – 11:50 AM, Location: West Ballroom
Whether you are a self-taught SAS learner with a lot of experience, or a novice just entering the SAS universe, you may not have spent a lot of time delving into two fantastic SAS superpowers. The Program Data Vector (PDV) is where SAS processes one observation at a time, in memory. The Data Step Debugger is an excellent tool to actually see the observation being held in memory and watch the movement of data from input to memory to output. Combining these two tools supplies SAS practitioners a lot of utility to “get under the hood” of how SAS code works in practice to ingest and analyze data during program operations. Once you know the specifics of what happens during compile time / execution, joins, and creating arrays, efficient SAS code will be at your fingertips. Action packed with animations, live demos and a great hands-on section, this presentation will likely be a resource that you will use and reuse now and in the future.
MT-200 : Sandwich your SAS datasets to Excel Pivot Tables
Charu Shankar, SAS Institute
Friday, 11:30 AM – 11:50 AM, Location: West Ballroom
Excel is universally loved. SAS has a way to bring excel into SAS so that you can analyze your data. Users now ask, “Great, I can analyze my data in SAS, but my end users don’t have SAS on their desktops. How can I give them SAS data in excel form”. We’ll go even further, instead of taking SAS into a standard Excel workbook, what if you could take SAS to an excel pivot table? Now you can. In this demo watch how quickly you can take a SAS dataset to excel pivot tables. See how in minutes, the Excel table shapes and forms right under your own eyes.
MT-201 : Code Smarter, Not Harder: Supercharging Your SAS Workflow with VS Code
Melodie Rush, SAS
Wednesday, 2:30 PM – 2:50 PM, Location: West Ballroom
Whether you’re a seasoned SAS programmer or just getting started, this Super Demo will show you how to modernize your coding experience using Visual Studio Code (VS Code). In just 20 minutes, you’ll learn how to connect VS Code to SAS Viya or SAS 9.4 and take advantage of a clean, flexible interface that supports efficient, organized, and reproducible SAS programming. We’ll cover how to: Set up the SAS extension for VS Code Use SAS Notebooks to run code line-by-line or by cell Organize your workflow using integrated project management and Git support Speed up your coding with syntax highlighting and inline documentation No matter your level of SAS experience, you’ll leave this session ready to boost your productivity and streamline your development process with the power of VS Code.
MT-202 : SAS Analytics Pro for Viya: Powering SAS on Your Desktop
Chris Hemedinger, SAS
Thursday, 10:30 AM – 10:50 AM, Location: West Ballroom
Discover how to bring the power of SAS Viya to your local machine with SAS Analytics Pro. This Super Demo showcases how to run SAS Viya in a Docker container and seamlessly integrate with SAS Enterprise Guide and Visual Studio Code. Learn how to develop, run, and manage SAS programs locally no server required.
MT-205 : Better (Prompt) Engineering: Leveraging ChatGPT to get started with SAS Coding
Melodie Rush, SAS
Wednesday, 3:00 PM – 3:20 PM, Location: West Ballroom
Learning a new coding language can be daunting. But, with your new personal research assistant, ChatGPT, it doesn’t have to be! In this session, we’ll meet you where you’re at as a coder. Perhaps you know R or Python well – and want to write SAS code. Or perhaps you’re truly getting started with SAS coding. Either way, learn how we can leverage LLMs to write the first round of code for us… or, better yet, have it help you understand code that someone has written. Either way, LLMs are a wonderful resource!
MT-206 : Data Validation Using SAS Procedures
Charu Shankar, SAS Institute
Friday, 11:00 AM – 11:20 AM, Location: West Ballroom
This session will focus on gathering both metadata & data from your SAS session to help make for faster code writing. Make the best use of these multi-talented PROCS that are not just for statisticians. use them to advantage. In this session the focus will be to learn to use interesting features of these 5 valuable PROCS: PROC FREQ PROC MEANS PROC UNIVARIATE PROC SORT PROC SQL
Open Source Solutions
OS-116 : How did that Python code get in my SAS program?
Jim Box, SAS Institute
Wednesday, 2:30 PM – 2:50 PM, Location: Writers
Python is a powerful Open Source programming language that is integrated SAS Viya, making it easy to switch between languages as needed. We’ll take a look at PROC PYTHON and how it adds SAS-specific methods, and explore an example utilizing SAS and Python together.
OS-176 : Car Price Prediction Using Machine Learning Techniques
Leon Davoody, Student
Lida Gharibvand, Loma Linda University
Thursday, 8:30 AM – 8:50 AM, Location: Writers
The Project which we are doing is about Car Price Prediction. So, our Prediction project uses the Data that is collected from Kaggle (Open-source website where you can find the datasets and codes). To make a Machine learning prediction model we need to perform Data Cleaning (Unnecessary information should be cleaned) and we need to focus on the parameters/Features are needed then we are grabbing parameters and training the model and finally giving the new input to the model and checking for the output.
OS-178 : R Made Easier for SAS Programmers
Sunil Gupta, Gupta Programming
Wednesday, 3:00 PM – 3:50 PM, Location: Writers
With the trend of R in the pharma industry, it makes sense for SAS programmers to seriously consider adding R in their skill set. Smarter organizations are considering using R for qc purpose and SAS for source programming. While R has a steep learning curve since it is more object-oriented programming language, there are many parallelisms that can be drawn with SAS. This presentation will showcase and compare R with SAS in data import, management, visuals and analysis. Common R packages and examples will be compared with SAS functions and macros to help SAS programmers make the transition easier to R. In addition, packages unique to R such as pharmaverse will also be reviewed.
OS-203 : Next Level Reporting: ODS and Open Source
Chevell Parker, SAS Institute
Wednesday, 4:00 PM – 4:50 PM, Location: Writers
This presentation describes techniques used to get started with the Output Delivery System such as an introduction to the Output Delivery System basics. Also discussed include automating and making the most out of your ODS destinations. This document will also highlight the dynamic capabilities when using ODS. The document will further drill down on the popular ODS Excel destination and discuss highlights and limitations of this destination. Finally, this document will discuss open-source integration in SAS and Viya platforms and how this can be used to take your reporting to the next level. Concepts will focus on techniques geared to the data analyst and SAS programmer.
OS-204 : Friends with Benefits: SAS , SQL, R, and Python in SAS Viya Workbench
Joe Madden, SAS
Thursday, 9:00 AM – 9:50 AM, Location: Writers
This hands-on workshop shows learners how they can seamlessly switch from SAS , SQL, R, and Python within a project under the mantra of “use the right tool for the job”. Or, more simply put: use the tool you know. This activity follows a new policy analyst at the Department of Health and Human Services, who is tasked with understanding how the coronavirus, i.e., COVID19, impacted the labor supply of prime-aged women across the United States. Along that policy journey, you’ll also gain exposure to SAS Viya Workbench, our latest-and-greatest coding environment!
Pharma and Healthcare
PH-104 : Worried about that Second Date with ISO ? Using PROC FCMP to Convert and Impute ISO 8601 Dates to Numeric Dates
Richann Watson, DataRich Consulting
Friday, 10:00 AM – 10:20 AM, Location: Directors
Within the life sciences, programmers often find themselves doing a lot of dating matching, converting between character and numeric values, and imputing missing components. Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) domains have implemented the use of the International Organization for Standardization (ISO) format, ISO 8601, for datetimes. These dates are stored as character strings with missing components denoted using a single hyphen. Although this format helps to standardize how dates and times are captured so that there is no confusion as to what the date represents, it leaves a longing for something more compatible for analysis purposes: determining durations and number of days from a reference point. The conversion of ISO dates to a numeric format requires a serious commitment, especially when partial ISO dates require imputations. Although SAS offers a variety of built-in formats and functions that can appease both sides on a date, i.e., converting complete ISO dates to numeric values or numeric dates to ISO dates, there is no SAS-provided function that will help with the required conversion and imputation of a partial date. Fortunately, with the use of the FCMP procedure, we can create our own custom functions to help achieve our desired goal. This paper illustrates the process of building a custom function that will take a date that is captured in the appropriate ISO format in SDTM (–DTC) and convert that date to a numeric format while also giving partial dates the extra attention to impute missing components. Additionally, this custom function sets the correct date imputation variable (–DTF), so you always know just how much of a blind date your derived value really is.
PH-127 : Have a Date with ISO ? Using PROC FCMP to Convert Dates to ISO 8601
Richann Watson, DataRich Consulting
Friday, 9:30 AM – 9:50 AM, Location: Directors
Programmers frequently have to deal with dates and date formats. At times, determining whether a date is in a day-month or month-day format can leave us confounded. Clinical Data Interchange Standards Consortium (CDISC) has implemented the use of the International Organization for Standardization (ISO) format, ISO 8601, for datetimes in SDTM domains, to alleviate the confusion. However, converting “datetimes” from the raw data source to the ISO 8601 format is no picnic. While SAS has many different functions and CALL subroutines, there is not a single magic function to take raw datetimes and convert them to ISO 8601. Fortunately, SAS allows us to create our own custom functions and subroutines. This paper illustrates the process of building a custom function with custom subroutines that takes raw datetimes in various states of completeness and converts them to the proper ISO 8601 format.
PH-154 : Prompt, Program, Submit: Generative AI for Faster SDTM, ADaM, and TLFs
Matt Becker, SAS
Thursday, 4:00 PM – 4:50 PM, Location: Directors
The life sciences industry is seeing more requests for quick, compliant clinical trial submissions, which makes it even more important to improve programming operations. Generative artificial intelligence, especially large language models (LLMs), could change the way SDTM, ADaM, and TLFs are created in a big way. This session will investigate the practical applications of generative AI to automate and enhance critical clinical programming duties. From the mapping of raw data to SDTM domains, to the crafting of ADaM specifications, and the generation of boilerplate code or statistical summaries, we will analyze real-world use cases. These examples will demonstrate how to reduce manual effort while ensuring traceability and compliance. Additionally, we will illustrate the integration of these AI-driven procedures into SAS environments to improve productivity without compromising regulatory compliance. Whether you are a statistical programmer, data manager, or biostatistician, this session will help you reimagine what is possible when human expertise meets machine intelligence.
PH-163 : Around the Data DOSE-y Doe, How Much Fun Can Your Data Can Be: Using DOSExx Variables within ADaM Data Sets
Inka Leprince, PharmaStat, LLC
Richann Watson, DataRich Consulting
Friday, 8:30 AM – 8:50 AM, Location: Directors
In the intricate dance of clinical trials that involve multiple treatment groups and varying dose levels, subjects pirouette through planned treatments – each step assigned with precision. Yet, in the realms of pediatric, oncology, and diabetic trials, the challenge arises when planned doses twirl in the delicate arms of weight-adjustments. How can data analysts choreograph the Analysis Data Model (ADaM) data sets to capture these nuanced doses? There is a yearning to continue with the normal dance routine of analyzing subjects based on their protocol-specified treatments, yet at times it is necessary to learn a new dance step, so as not to overlook the weight-adjusted doses the subjects actually received. The treatment variables TRTxxP/N in the Subject-Level Analysis Dataset (ADSL) and their partners TRTP/N in Basic Data Structure (BDS) and Occurrence Data Structure (OCCDS) are elegantly designed to ensure each treatment glides into its designated column in the summary tables. But we also need to preserve the weight-adjusted dose level on a subject- and record-level basis. DOSExxP and DOSExxA, gracefully twirl in the ADSL arena, while their counterparts, the dashing DOSEP and DOSEA, lead the waltz in the BDS and OCCDS data sets. Together, these harmonious variables pirouette across the ADaM data sets, capturing the very essence of the weight-adjusted doses in a dance that seamlessly unfolds.
PH-164 : From RealityTV to Real Dependency: Digital Validation Dependence Disorder (DVDD)
Denzel Smalls Smalls, Word of Mouth Production llc
Friday, 11:30 AM – 11:50 AM, Location: Directors
Background& PurposeReality television normalized constant selfdisclosure, manufactured conflict, and audiencedriven approval, reshaping cultural expectations of attention and validation. These dynamics have migrated to social platforms, fostering what we term Digital Validation Dependence Disorder (DVDD) a pattern of compulsive posting, “like” addiction, and mood swings tied to online feedback. MethodsThis paper presents a simulated dataset (n=5,000) capturing DVDDrelated variables: daily post frequency, timeonline duration, engagement volatility, branding consistency, moodscore swings, and offline wellbeing. Using SAS procedures that can be sued PROC CORR, PROC REG, PROC CLUSTER, PROC LOGISTIC, and PROC HPSPLIT we (1) quantify associations between validationseeking behaviors and wellbeing, (2) identify latent DVDD subtypes, and (3) build predictive models flagging highrisk users. ImplicationsFindings illustrate how SAS can bridge media psychology and data science, offering a replicable workflow for mentalhealth screening in digital contexts. The study also highlights how realityTVinspired behaviors underpin modern socialmedia dependence, informing prevention strategies for clinicians, educators, and platform designers. ContributionBy pairing a culturally grounded theory with robust statistical programming, this paper invites the WUSS community to explore emerging interdisciplinary domains where SAS analytics can drive societal impact.
PH-168 : Automating Birth Weight for Gestational Age Classification: Fenton Calculator SAS Macro
Mehrnaz Siavoshi, Walden University
Friday, 11:00 AM – 11:20 AM, Location: Directors
The Fenton growth chart provides sex-specific birth weight references by gestational age for infants born preterm and is widely used in clinical and research settings. Currently available as Excel and web-based calculators, these tools require manual entry for individual cases, limiting their utility for large-scale research applications. This paper presents a compiled SAS macro that automates the application of the Fenton growth chart to calculate weight-for-gestational-age Z scores and percentiles using the LMS method. The macro classifies infants into small (SGA), appropriate (AGA), or large (LGA) for gestational age categories based on standardized percentile cutoffs. This automation enables rapid batch analysis of birth weight data seamless integration into existing SAS analytical workflows.
PH-175 : Implementing Sequence Analysis For Lifecourse Epidemiology in SAS: A Practical Guide
Mehrnaz Siavoshi, Walden University
Thursday, 5:00 PM – 5:50 PM, Location: Directors
Health research and epidemiology have long established socioeconomic position as an important predictor of health outcomes. However, the growing field of lifecourse epidemiology seeks to understand how changes in these predictors over the course of a lifetime impact health outcomes. Sequence analysis is an increasingly used methodology in lifecourse epidemiology to characterize longitudinal trajectories of exposures. This paper presents a practical SAS implementation of sequence analysis based on the comprehensive methodology in R and Stata described by Pacca et al. (2024). The methodology includes three core steps: creating individual trajectories, quantifying trajectory dissimilarity, and performing cluster analysis to group similar trajectories between individuals. Using a simulated retirement dataset, this paper demonstrates how standard SAS procedures can be adapted for sequence analysis with PROC IML. A discussion of the advantages and limitations of using SAS for this technique is provided and full SAS code is available.
PH-183 : Quality Metrics to Assess Risk-Based Testing
Bill Coar, Axio, a Cytel Company
Friday, 9:00 AM – 9:20 AM, Location: Directors
Trends in the regulatory landscape point to risk-based approaches to ensure high quality data and reporting for clinical trials. Risk-based methods for validation of production programming code which assign testing methods of varying robustness based on an assessment of risk have been evaluated and accepted by some industry leaders. Risk based testing does not attempt to achieve perfection. They primarily focus on spending more time on those areas where mistakes tend to be more consequential. Metrics both within and across projects can be implemented to aid in this evaluation. They can report the incidence, type, and method of identification of issues found at various timepoints in the production process. This includes issues found internally prior to the completion of output verification (i.e., during testing), internally during final package review, and during external review. These data are crucial for the effective evaluation of the performance of risk-based testing methods and decisions.
PH-194 : Automating Clinical Study Reports: A Modular Framework for Continuous Data Reporting
William Wei, Merck & Co., Inc.
Friday, 10:30 AM – 10:50 AM, Location: Directors
Enhancing efficiency while ensuring high-quality work remains a central focus in clinical programming. With the advancement of CDISC standards, the automation of ADaM datasets and reports has become increasingly feasible and practical. A table, listing, and figure (TLF) package integrated into a clinical study report (CSR) effectively summarizes data from clinical trials, with tables making up the bulk of this package. The CSR tables, which primarily present safety and efficacy data, can be divided into two major categories: 1) categorical (e.g., gender, sex, race) and 2) continuous (e.g., age, time to event). The CSR integrates these two main categories along with additional ones. Automation of the CSR can be accomplished by combining various category modules. Each module discussed here is generated automatically and dynamically based on actual data. SAS offers several advantages for modular programming in this context. Its robust data handling capabilities and extensive library of statistical procedures make it ideal for processing clinical trial data efficiently. The categorical module was previously introduced in our previous paper titled “Semi-Automated and Modularized Approach to Generate Tables for Clinical Study Categorical Data Report”. This paper focuses on the design of the continuous module, which can produce multiple output formats to satisfy reporting needs. This module is designed to be independent, flexible, portable, informative, and parameter-driven, allowing for easy export or linkage to various reports. This can help automate production of continuous table reports in different formats, enabling the combination of reports into more complex tables. For example, a table may include blocks for categorical analysis, continuous data, and additional elements such as confidence intervals and p-values. This methodology offers users significant flexibility in constructing tables according to specifications and presenting them as desired. Furthermore, this approach greatly reduces the time and effort needed for programming CSR tables.
Solutions Development
SD-109 : Last Observation Carried Forward (LOCF) in Longitudinal Clinical Studies: Adopting a Functional Approach to Imputing Missing Values Using PROC FCMP, the SAS Function Compiler
Troy Hughes, Data Llama Analytics
Thursday, 11:00 AM – 11:20 AM, Location: Writers
Last observation carried forward (LOCF) is a ubiquitous method of imputing missing values in longitudinal studies, and is commonly implemented when a subject (i.e., a patient or participant) misses a scheduled visit, and data cannot be collected (or generated). In general, the last “valid” value from a previous visit is retained for the later visit on which the data could not be obtained, and this conservative estimation succeeds in cases where the actual value would have been little changed. Nuanced criteria may stipulate which prior values count as “valid” (e.g., after the start of treatment) as well as for how long (e.g., how many days, visit weeks, consecutive missed visits) a value can be used to impute other values. Given these complexities, LOCF solutions implemented in SAS historically adopt a procedural approach, and often require multiple DATA steps and/or procedures to impute data both across observations and within subjects. Conceptually, however, a functional approach can be envisioned in which LOCF could be calculated using a function call that is, delivering the same functionality through a single line of code while hiding (abstracting) the complexity of the calculation inside the function’s definition. The FCMP procedure can deliver this functionality, enabling SAS practitioners to build user-defined functions even those that perform inter-observation calculations and this text demonstrates a user-defined subroutine that dynamically calculates LOCF while relying on CDISC and ADaM standards, data structures, and nomenclature. The software design concepts herein are adapted from the renowned SAS Press book: PROC FCMP User-Defined Functions: An Introduction to the SAS Function Compiler. (Hughes, 2024)
SD-117 : Share your Macros and Programs via a SAS Studio GUI Interface
Jim Box, SAS Institute
Thursday, 11:30 AM – 11:50 AM, Location: Writers
You’ve probably got a huge macro and programming library at your disposal, but no way of leveraging all of that capital in any way but inside a SAS program. We’ll look at how using SAS Custom Steps and Flows will allow to unlock the full potential of your work. By leveraging these capabilities, non-programmers can seamlessly run analyses and other complex processes that you built with minimal training and intervention on your part. This integration not only enhances collaboration but also democratizes access to powerful analysis tools, enabling a broader audience to contribute to data-driven decision-making. Through practical examples and detailed guidance, this paper showcases the simplicity and efficiency of converting SAS macros into user-friendly tools, allowing programmers and non-programmers alike to get the value of the solutions you have created.
SD-138 : From Muggles to Macros: Transfiguring Your SAS Programs with Dynamic, Data-Driven Wizardry
Josh Horstman, PharmaStat LLC
Richann Watson, DataRich Consulting
Thursday, 2:30 PM – 3:20 PM, Location: Writers
The SAS macro facility is an amazing tool for creating dynamic, flexible, reusable programs that can automatically adapt to change. This presentation uses a series of examples to demonstrate how to transform static “muggle” code full of hardcodes and data dependencies by adding macro language magic to create data-driven programming logic. Don’t hardcode data values into your programs. Cast a vanishing spell on data dependencies and let the macro facility write your SAS code for you!
SD-139 : More Muggles, More Macros: Adding Advanced Data-Driven Wizardry to Your SAS Programs
Josh Horstman, PharmaStat LLC
Richann Watson, DataRich Consulting
Thursday, 4:00 PM – 4:50 PM, Location: Writers
In their popular 2024 presentation “From Muggles to Macros”, Horstman and Watson delivered a spell book full of macro magic to enhance SAS programs with data-driven wizardry. This exciting sequel to that enchanting performance adds to the list of incantations for creating dynamic, flexible, reusable programs that can automatically adapt to change. New charms include the use of control tables, the CALL EXECUTE routine, and of course, more macro language techniques. Don’t hardcode data values into your programs. Cast a vanishing spell on data dependencies and let the macro facility write your SAS code for you!
SD-157 : Leveraging SAS DATA Step to Evaluate Government Programs by Repurposing Administrative Data: Strategies for Narrowing Data Scope, Imputing Missing Data, Mitigating Bias, and Streamlining Reporting
Bert Cisneros, Arizona Supreme Court – Adminstrative Office of the Courts
Richard Rivera, Arizona Supreme Court
Thursday, 10:00 AM – 10:50 AM, Location: Writers
Administrative datasets collected for operational purposes often present structural and analytical challenges for SAS users conducting impact or outcome studies. This session demonstrates how native SAS DATA Step programming was used to transform 6.5 million Arizona court records sourced from both defensive driving and court case management databases into comparable cohorts for evaluating the impact of Arizona’s Defensive Driving School Program on traffic re-offense rates. This session outlines four core strategies developed through native DATA Step programming to address structural challenges and enable impact analysis: 1) Narrowing data scope through inclusion criteria and tailored recoding logic; 2) Imputing missing data via a name-based gender assignment algorithm; 3) Mitigating sampling bias using a multi-step matching process in pure DATA Step logic to build a comparable study cohort; and 4) Streamlining reporting with multidimensional arrays that scale across groups and time intervals eliminating the need for major rewrites as reporting needs evolve. The presentation concludes with key research findings from the traffic recidivism study and highlights the relevance of these four methodological approaches in enhancing data processing efficiency, improving analytic precision, and supporting scalable reporting. Together, they offer a practical framework for SAS users working with large-scale, complex administrative data repurposed for analytical insight and impact studies.
SD-167 : From Excel Chaos to Power BI Clarity: Using SAS for Streamlined Public Health Dashboards
Alyssa Planas, Office of Analytics State of Nevada
Kanan Castro, NV Board of Pharmacy
Thursday, 2:00 PM – 2:20 PM, Location: Writers
Power BI is a powerful visualization tool, but its data cleaning and management features are often lacking, especially when working with messy Excel datasets from public health sources. This presentation shows how SAS can be leveraged as a preprocessing powerhouse handling data cleaning, reshaping, filtering logic, and structural harmonization so that Power BI can focus on what it does best: dashboarding.
SD-180 : Reach for R Low Hanging Fruit for Faster Results
Sunil Gupta, Gupta Programming
Wednesday, 5:30 PM – 5:50 PM, Location: Writers
With the pharma industry expanding packages within pharmaverse, is your organization ready to reach for R low hanging fruit for faster results? This presentation will show how R packages and functions are out of the box’ production ready for fast results without having to invest in SAS silo resources or expenses. While some R mentoring is required to understand key differences with SAS and R fundamental concepts, smarter organizations can get a jump start on their submission ready deliverables. R packages in data management and graphs enable plug-n-play’ queries and plots. The R packages used to create the Clinical Study Report (CSR) tables and lists offer R script templates that can be customized. CDISC submission support packages include the define.xml and tidyCDISC. Finally, popular pharmaverse packages rtables, admiral and teal will also be featured.
SD-184 : Fun With SAS and Emoji: What Might a Rebus-Influenced Programming Language Look Like?
Ted Conway, Self
Thursday, 5:00 PM – 5:20 PM, Location: Writers
Remember those fun Highlights for Children stories in which words were replaced with pictures to help and engage young readers? Ever wonder what that might look like in a programming language? In this session, we’ll not only take a whimsical look at some examples of rebus-flavored SAS and SQL code snippets but also demonstrate rudimentary SAS and Python preprocessors that translate emoji into executable code using SAS’s Unicode string ‘K’ functions and the Python regex package. This session is intended for all SAS users.