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WUSS 2025 Papers - Index

55 papers. Source: WUSS (Western Users of SAS Software) conference proceedings.

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1 A Dynamic Approach to Processing XML Files David B. Horvath The XML engine within SAS is very powerful but it does convert every object into a SAS data set 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 data sets, which 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. proc step, proc contents, proc import, dataset source 9
2 Accredited, Bona fide, Certified, Diploma'ed, and Edumacated: The ABCDEs of Automating the Validation and Monitoring of Professional Requirements for Employees and Job Candidates Through Dynamic, Data-Driven SAS Reporting Troy Martin Hughes 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. Whereas validation and monitoring professional requirements for a small team might be accomplished in minutes, the effort increases commensurate 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. data validation rules, sample, excel, xls, proc import, sas base, txt, dataset, data analyst, data cleaning, sas programmer source 20
3 Applications of PROC COMPARE to Parallel Programming and other projects Jayanth Iyengar 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. proc compare, proc step, dataset, sas log, missing value, sas output, numeric variable, proc sql, length statement, sas base, master data management, data cleaning source 21
4 Around the Data DOSE-y Doe, How Much Fun Can Your Data Can Be: Using DOSExx Variables within ADaM Data Sets Inka Leprince,
Richann J. Watson		 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. clinical trial, dose, treatment period, adam dataset, clinical research, clinical data, adverse events, data science, cdisc source 13
5 Automating Birth Weight for Gestational Age Classification: Fenton Calculator SAS Macro Mehrnaz Siavoshi 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 for seamless integration into existing SAS analytical workflows. macro definition, excel source 4
6 Automating Clinical Study Reports: A Modular Framework for Continuous Data Reporting William Wei 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 stu dy 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. interval variable, csr, clinical trial, nominal variable, confidence interval, macro parameter, proc step, cdisc source 7
7 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 Martin Hughes 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 attend ees. Conference badges, emblazoned with attendee -specific QR code s, can communicate attendee txt, sample source 15
8 Chasing Master Data Interoperability: Facilitating Master Data Management (MDM) Objectives Through CSV Control Tables that Contain Data Rules that Support SAS and Python Data-Driven Software Design Troy Martin Hughes Control tables are 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 many cases, nontechnical end users to alter software functionality without having to modify code. Moreover, when control tables are maintained within canonical file structures such as comma-separated values (CSV), extensible markup language (XML), or JavaScript object notation (JSON), these ubiquitous data structures facilitate master data interoperability by enabling one control table to drive not only SAS software but also non-SAS applications (such as Python). data validation rules, temporary array, dataset, macro definition, master data management, macro variable, array source 5
9 Conducting Survival Analysis in SAS using Medicare Claims as a Real-world data source Jayanth Iyengar 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. proc step, cpu time, survival analysis, proc phreg, survival function, proc lifetest, hazard ratio, predictor variable, proc sql, insurance claims, chi-square test, dataset, nominal variable, censoring, rank test, likelihood function, proc sgpanel, exploratory data analysis, data science, probability distribution, binary variable, hazard function, log-rank test, lookup, maximum likelihood estimation, proc means, proc sgplot, proc sort, create table, if-then-else, wilcoxon test, regression analysis, proc freq source 32
10 Data Validation Using SAS Procedures Charu Shankar Presentation proc freq, sas log, proc univariate, where statement, proc step, proc print, dataset source 62
11 Enhance your Coding Experience with the SAS Extension for VS Code Jim Box 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. sas 9, sas viya source 11
12 Evolving Human Emotions Under a Global Crisis: An Analysis of Popular Music Sentiment Hamed Hemmati,
Bennett Frohock		 Music serves as a powerful lens into the human experience, reflecting societal shifts and emotional undercurrents. This study investigates how the COVID-19 pandemic transformed the thematic and emotional landscape of popular music lyrics across three pivotal periods: Pre-COVID, During COVID, and Post-COVID. Utilizing data from Spotify and Musixmatch, we analyzed the top 400 songs from each era, employing advanced AI tools (GPT-4o-mini) and statistical methods to extract themes and emotional tones. Our findings reveal that during the pandemic, lyrics shifted toward introspective themes like education, work, and community, often laced with darker emotional tones. chi-square test, bar chart, bivariate analysis, feature extraction, data science, proc freq, sample, data cleaning source 23
13 ExCITE-ing! Build Your Paper's Reference Section Programmatically Using Lex Jansen's Website and SAS Louise S. Hadden 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 ePoster will demonstrate how to accomplish this goal. source 9
14 FDA says, Do the Allowable Total Difference Zone: A Method Comparison using the ATDzone SAS Macro Jesse Albert Canchola,
Natasha Oza		 Reader precision is a cornerstone of diagnostic accuracy evaluations, especially in clinical studies where human interpretation drives key outcomes. This paper delivers an in-depth narrative on advanced methodologies to assess both inter-reader (between-reader) and intra-reader (within- reader) variability. It synthesizes theoretical underpinnings, statistical techniques, and practical steps for implementing robust analyses in real-world diagnostic contexts. We highlight the application of weighted agreement metrics, adjusting for reader expertise and case difficulty, to ensure reproducibility, regulatory compliance, and clinical validity. R and SAS code examples, coupled with a reproducible dataset, enable readers to apply these methods directly in their own work. sensitivity analysis, create table, washout period, proc step source 22
15 From Excel Chaos to Power BI Clarity: Using SAS for Streamlined Public Health Dashboards Alyssa Planas,
Kanan Castro		 Power BI works best when it's given clean, well-structured data-allowing analysts to focus on building clear, interactive dashboards without getting bogged down in cleanup. For public health professionals working with messy or inconsistent EXCEL files, preparing that data beforehand can make the entire dashboarding process faster and more reliable. This paper highlights how SAS can be used as a powerful preprocessing tool to clean, reshape, and standardize data sets before importing them into Power BI. By using SAS to handle the prep work, analysts can streamline their workflow, reduce dashboard errors, and make Power BI easier to use-especially for those new to the platform. excel, data transformation source 7
16 From Muggles to Macros: Transfiguring Your SAS Programs With Dynamic, Data-Driven Wizardry Joshua M. Horstman,
Richann J. Watson		 Presentation macro variable, proc sql, variable list, macro facility, proc step, automatic macro variable, macro loop source 52
17 From Newcomer to Networker: Mastering Soft Skills Alyssa Planas As a new professional navigating the data analytics field, attending conferences like WUSS and PharmaSUG has been instrumental for my career growth. Beyond technical sessions, these events offer valuable opportunities to connect, learn, and grow professionally. In this paper, I will share practical strategies on how to effectively network and engage at 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. Drawing on personal experiences, this paper aims to empower new professionals to leverage these gatherings to advance their careers. source 7
18 From RealityTV to Real Dependency: Digital Validation Dependence Disorder (DVDD) Denzel Smalls Smalls Digital Validation Dependence Disorder (DVDD) is a conceptual mental health condition that emerges from the intersection of social media culture, identity formation, and behavioral reinforcement. DVDD reflects the growing societal reliance on external affirmation through digital metrics such as likes, comments, and shares. This paper explores DVDD as both a cultural critique and a framework for behavioral modeling, using SAS to operationalize measurable indicators including posting frequency, engagement-mood correlation, and identity drift scores. The discussion draws parallels between the rise of reality television and the attention economy, illustrating how entertainment media normalized performance-based identity and validation-seeking behaviors. The paper concludes by addressing ethical implications, potential diagnostic criteria, subtypes, and strategies for early detection and intervention. correlation, data cleaning, proc freq, proc step source 15
19 Fun With SAS and Emoji: What Might a Rebus-Influenced Programming Language Look Like? Ted Conway 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 paper, we'll not only take a whimsical look at some examples of rebus-flavored SAS and SQL code snippets, but also look at some rudimentary SAS and Python preprocessor code snippets to translate programs with emojis back into executable code using both SAS's Unicode string 'K' functions and the Python regex package. proc sql, macro variable source 8
20 Future trends in AI and impacts on SAS Programming David B. Horvath AI is rapidly growing in usage and the rate seems to be accelerating. This paper discusses: Where we are today Where we are going * Then what? Along with a discussion of how it relates to SAS tools. machine learning, training set source 5
21 Getting Started with SAS Visual Analytics Danny R. Modlin 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. bar chart, proc freq, gradient boosting source 4
22 Going to great lengths: Using VARCHARs in the DATA step Richard D. Langston This paper describes the use of VARCHARs within the DATA STEP through various examples. This feature was added to 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. character variable, macro variable, length statement, character functions source 14
23 Have a Date with ISO? Using PROC FCMP to Convert Dates to ISO 8601 Richann J. Watson 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. proc fcmp, proc step, return, sas log, sample, time., numeric variable, cdisc source 15
24 Highlighting the Differences: PROC COMPARE in Excel Jane Eslinger 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 doesn't 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 strategically uses color to highlight the details and make variable-level differences easy for the reviewer to spot. proc compare, proc step, binary variable, excel, numeric variable, proc report, character variable, macro parameter, do loop, data cleaning, macro variable, macro loop source 12
25 How did that Python code get in my SAS program? Jim Box Did you know that you can execute Python code inside a SAS Program? With the SAS Viya Platform, you can call PROC PYTHON and pass variables and datasets easily between a Python call and a SAS program. In this paper, we will look at ways to integrate Python in your SAS Programs. sas log, proc step, proc python, macro variable, dataset, histogram source 7
26 Implementing Sequence Analysis for Life Course Epidemiology in SAS: A Practical Guide Mehrnaz Siavoshi Health research and epidemiology have long established socioeconomic position as an important predictor of health outcomes. However, the growing field of life course 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 life course 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. cluster analysis, sample, hierarchical clustering, proc step source 7
27 Inter-reader Variability and Reader Weighting in Diagnostic Accuracy Studies using the SAS System Jesse Albert Canchola,
Monica Vasquez,
Payam Ahmadi		 Reader precision is a cornerstone of diagnostic accuracy evaluations, especially in clinical studies where human interpretation drives key outcomes. This paper delivers an in-depth narrative on advanced methodologies to assess both inter-reader (between-reader) and intra-reader (within- reader) variability. It synthesizes theoretical underpinnings, statistical techniques, and practical steps for implementing robust analyses in real-world diagnostic contexts. We highlight the application of weighted agreement metrics, adjusting for reader expertise and case difficulty, to ensure reproducibility, regulatory compliance, and clinical validity. R and SAS code examples, coupled with a reproducible dataset, enable readers to apply these methods directly in their own work. sensitivity analysis, create table, washout period, proc step source 22
28 Large-Scale Time Series Forecasting in Model Studio Danny R. Modlin In this hands-on workshop, 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. time series analysis, champion model, time series data, sas viya, data mining, by group processing, forecast horizon, feature selection, predictor variable, score code, seasonality source 39
29 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 Martin Hughes 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. imputation, proc fcmp, missing value, data validation rules, last observation carried forward, retain source 9
30 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 Humberto Cisneros,
Richard Rivera		 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. proc step, proc print, case management, follow-up period, proc format, calendar year, imputation source 16
31 Making a Readable PROC COMPARE Report in Excel Jane Eslinger 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. It then shows how to use those data sets with PROC REPORT to create a cleaner, more readable report in EXCEL perfect for review, documentation, or delivery. proc compare, proc step, excel, macro variable, proc print, output delivery system, ods output, data cleaning, proc sql, ods excel, macro definition source 27
32 Monetize Your SAS Insights Karen Walker 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 Management: 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. sas studio, data science, correlation source 9
33 More Muggles, More Macros: Adding Advanced Data-Driven Wizardry to Your SAS Programs Joshua M. Horstman,
Richann J. Watson		 Presentation macro variable, call execute, proc step, macro call, macro facility, ods rtf, proc sgplot, output delivery system, variable list, change from baseline, macro definition, safety population, &variable, proc sql, excel, macro loop source 45
34 PFS2, did you hear about it too? - Understanding the derivation of Progression Free Survival on next-line therapy (PFS2). Yeshashwini Chenna,
Lupetu Ntambwe		 In Clinical Trials, Endpoints are important milestones for determining how effective the 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 PFS2 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. adam dataset, date variable, randomization, survival analysis, sample, clinical trial, create table, disease response, proc sql, cdisc source 11
35 PROC BGLIMM: The Smooth Transition to Bayesian Analysis Danny R. Modlin Presentation covariance, proc step, proc mixed, output delivery system, sample, covariance structure, cpu time, monte carlo, ods graphics, poisson distribution, regression analysis source 57
36 Prompt, Program, Submit: Generative AI for Faster SDTM, ADaM, and TLFs Matthew J. Becker 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 (GenAI), 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 GenAI 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 common code or statistical summaries, we will analyze real-world use cases. clinical programming, clinical trial, life sciences, raw data, reviewer guide, data science, mock shell, cdisc source 7
37 R Made Easier for SAS Programmers Sunil Kumar Gupta 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. pharmaceutical industry, macro definition, sas programmer source 4
38 Reach for R Low Hanging Fruit for Faster Results Sunil Kumar Gupta 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. clinical trial, csr, pharmaceutical industry, cdisc source 4
39 Rerun or Reboot? Reproducibility in a Golden Age of Generative AI Wendy Christensen 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. missing value, artificial intelligence source 5
40 Sandwich your SAS datasets to Excel Pivot Tables Charu Shankar Presentation excel, output delivery system, dataset, proc step, macro variable, libname statement, proc freq source 28
41 SAS Essentials I: How SAS Thinks Susan J. Slaughter Presentation dataset, libname statement, proc step, sas studio, date variable, sas base, sas log source 38
42 SAS Essentials II: Introduction to DATA Step Programming Susan J. Slaughter Presentation raw data, dataset, set statement, input statement, proc step, sas log, if-then-else source 40
43 SAS Essentials III: Introduction to SAS Procedures Susan J. Slaughter Presentation proc step, proc contents, proc freq, proc print, proc sort, output delivery system, proc format source 47
44 SAS, AI, and Me: Connecting SAS with AI to Become Almost Superhuman Steven C. Black 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, facilitating the transfer of datasets and code to gain profound AI -driven insights. Furthermore, I'll highlight diverse 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 . dataset, proc step, macro variable, macro definition source 25
45 Share your Macros and Programs via a SAS Studio GUI Interface Jim Box 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 you 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, ultimately fostering an inclusive and efficient research environment. macro variable, adverse events source 10
46 So You Want To Be An Independent Consultant: 2025 Edition Joshua M. Horstman 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 issue s. The paper has been recently updated to reflect the new realities of independent consulting in 202 5 and beyond. artificial intelligence, auto insurance source 12
47 Sugar Rush: SQL Master Class for Pharma Professionals Charu Shankar Presentation proc sql, proc step, group by, select, where statement, data step, dictionary tables source 32
48 The (ODS) Output of Your Desires: Creating Designer Reports and Data Sets Louise S. Hadden 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. proc contents, proc step, ods output, sas output, estimate, output, excel, data step, proc univariate, odds ratio, proc sql, proc surveyfreq, proc report source 12
49 The GENMOD Toolkit: What do you do when things aren't Normal? Danny R. Modlin Presentation poisson distribution, proc genmod, proc calis, likelihood function, maximum likelihood estimation, poisson regression, binomial distribution, regression analysis, negative binomial distribution, response variable, proc step, correlation source 36
50 The Program Data Vector and Data Step Debugger Charu Shankar Presentation hash table, array, clinical programming, proc sql, pdv, data step, where statement source 36
51 The Role of Congruity and Gendered Leadership Expectations in Higher Education: A Meta-Analysis of Bias and Leadership Evaluations Among Women Leaders Mehrnoosh Golshahifar This meta-analysis examines gendered leadership evaluations in higher education, using Role Congruity Theory (Eagly & Karau, 2002) to explain prejudice against women leaders when leadership roles are seen as incompatible with traditional feminine traits. The central question asks: To what extent does role congruity bias influence evaluations of women leaders in higher education? Six peer-reviewed quantitative studies (2022-2024) met strict inclusion criteria, requiring gender-disaggregated leadership evaluation data from higher education settings. Data were synthesized using group means, standard deviations, t-tests, and effect sizes (Cohen's d, Hedges' g) to assess how gendered expectations shape perceived competence-particularly transformational leadership-and leader emergence. Effect sizes ranged from -0.28 to -0.50, with a pooled estimate of -0.40 (95% CI -0.47 to -0.33), indicating small-to- medium bias. effect size, sample, standard deviation, forest plot, sample size, regression analysis source 13
52 The World is Not Enough: Base SAS Visualizations and Geolocations Louise S. Hadden 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. proc step, sas base, proc gmap source 12
53 Validate the Code, not just the Data : A System for SAS program validation Jayanth Iyengar 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. cpu time, dataset, proc step, sas log, proc print, proc sql, excel, key performance indicator, proc import, flag variable, macro variable, txt, call symput, proc transpose, xls, input statement, proc contents, proc sort, if-then-else, create table, fullstimer, data step, data cleaning source 38
54 Worried about that Second Date with ISO? Using PROC FCMP to Convert and Impute ISO 8601 Dates to Numeric Dates Richann J. Watson 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. imputation, proc fcmp, proc step, return, sas function, adverse events, options statement, cdisc source 19
55 You've Got Options: Five-Star SAS System Option Hacks Louise S. Hadden 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 XWAIT. 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 have seen, used, and automatically included in their code. This presentation is suitable for all skill and experience levels; platform and implementation differences are part of the discussion. options statement, proc step, proc options source 11

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