| SESUG 2024 Conference Proceedings |
| Track |
|---|
| Careers, Training, and Education (CTE) |
| Data Collection, Management, and Manipulation (DCMM) |
| Foundational SAS Skills (FSS) |
| Hands-on Workshops (HOW) |
| Intermediate and Advanced SAS Skills (IASS) |
| Statistics, Modeling, and Analytics (SMA) |
| Visualization and Reporting (VR) |
| Careers, Training, and Education (CTE) |
| Paper | Authors | Title | Key Takeaways |
|---|---|---|---|
| Paper 130 | Josh Horstman and Richann Jean Watson | Adventures in Independent Consulting: Perspectives from Two Veteran Consultants Living the Dream | |
| Paper 142 | Troy Martin Hughes | Who's Bringing That Big Data Energy? A 47-Year Longitudinal Analysis of 30,000 Presentations in the SAS® User Community To Elucidate Top Contributors and Rising Stars | |
| Paper 146 | Muslihat Adejoke Gaffari | A Deep Dive into the Interplay of Depression, Sleep Deprivation, and Self-Esteem: An Application of an In-depth Advanced Statistical Analysis | |
| Paper 147 | Victor Aderanti | Investigating the Role of Radiomics in PET Image Analysis for Improved T-Stage Tumor Staging | |
| Paper 148 | Shanice Douglas | Forecasting U.S. Unemployment Rates Using ARIMA: A Time Series Analysis from 1948 to 2019 | |
| Paper 151 | zhongshan cheng | An Integrative Analyzer for Publicly Available COVID-19 Genome-wide Association Studies | |
| Paper 23 | Kirk Paul Lafler, Ryan Paul Lafler, Stephen Sloan and Joshua J. Cook | Benefits, Challenges, and Opportunities with Open-Source Software Integration | |
| Paper 51 | Tamar C. Roomian | From Good to GREAT: The (Not-So-Technical) Qualities of Great Programmers | |
| Paper 64 | Jayanth Iyengar | SAS Job Searching and Interviewing tips – Strategies in the Post-Pandemic era | |
| Paper 74 | Hongbo Li | The Essentials Given - Becoming an Expert of Statistical Programming in Pharma & Health Care Industry | |
| Paper 97 | Kelly Smith | Navigating Your Career: Interview Prep, Process, and Evaluation | |
| Data Collection, Management, and Manipulation (DCMM) |
| Paper | Authors | Title | Key Takeaways |
|---|---|---|---|
| Paper 108 | Guowei Zhao and Hong Qi | Applying Different Data Cutoff Dates for the Analysis of a Study Milestone | |
| Paper 123 | Ranjan Sitaula | Fontcase Macro | |
| Paper 145 | Alan Mann and Brett C. Peppe | Lions, Tigers and Documentation??? | |
| Paper 29 | Kirk Paul Lafler, Shaonan Wang, Nuoer Lu, Zheyuan Walter Yu, Daniel Qian and Swallow Xiaozhe Yan | Data Access Made Easy Using SAS® Studio | |
| Paper 34 | david horvath | Thinking outside the box: Leveraging "UNIX Tools" (GNU) for Data Analysis and Validation | |
| Paper 49 | Anupriya Lakshmipathy | C-SSRS Questionnaire Dataset Design for Assessing Suicide Risk in Neuroscience Studies | |
| Paper 56 | Brooke Ellen Delgoffe | REDCap © Your SAS Friend: Playing around in the API Playground | |
| Paper 65 | Jayanth Iyengar | NHANES Dietary Supplement component: a parallel programming project | |
| Paper 75 | Neesha Nathwani and Sarah Woodruff | Comparative Tools in SAS: Analyzing Self-Reported versus Imputed Values | |
| Paper 90 | Abdullah Khan | Using SAS In Analyzing Trends in Corn Yields, Surface Temperature, and Precipitation in U.S. South Atlantic Census Division States | |
| Foundational SAS Skills (FSS) |
| Paper | Authors | Title | Key Takeaways |
|---|---|---|---|
| Paper 101 | Kelly Smith | SAS and Analytical Research: A Primer for Qualitative Analysis | |
| Paper 102 | Emily F. Zucker | The Dreaded Data Type Conversion, Character to Numeric, Numeric to Character, a Deep Dive. | |
| Paper 106 | William Smith | Utilizing SAS to Create HTML Codebooks: Part Two! | |
| Paper 109 | Bruce Nawrocki | Using Visual Studio Code to Write SAS Code - A Primer | |
| Paper 139 | Chevell Parker | Tales From a Tech Support Guy: The Top Ten Most Impactful Reporting and Data Analytic Features for SAS Programmers | |
| Paper 24 | Kirk Paul Lafler | The 5 CATs in the Hat – Sleek Concatenation String Functions | |
| Paper 26 | Kirk Paul Lafler, Charu Shankar, Richann Jean Watson and Josh Horstman | The Battle of the Titans: DATA Step versus PROC SQL | |
| Paper 32 | Dane A. Korver | A Gentle Introduction to Creating SAS and R Graphs | |
| Paper 69 | Mark Jordan | Five Easy Ways to Make Your SAS Code Run Faster | |
| Paper 80 | Megan Christine Vaughn and Bailey M. Glidden | Backpacking Through the World of SAS PROC PRINT | |
| Paper 94 | Jason L. Ford | Introduction to Character Strings | |
| Paper 96 | Peter Knapp | Log Reviewing Made Easy | |
| Hands-on Workshops (HOW) |
| Paper | Authors | Title | Key Takeaways |
|---|---|---|---|
| Paper 104 | Mark Jordan | Data Diagnostics with Base SAS® | |
| Paper 115 | Jason Brinkley | Introduction to Clustering Algorithms | |
| Paper 143 | Troy Martin Hughes | Hands-on Python PDFs: Using the pypdf Library To Programmatically Design, Complete, Read, and Extract Data from PDF Forms Having Digital Signatures | |
| Paper 67 | Richann Jean Watson and Josh Horstman | Complex Custom Clinical Graphs Step by Step with SAS® ODS Statistical Graphics | |
| Intermediate and Advanced SAS Skills (IASS) |
| Paper | Authors | Title | Key Takeaways |
|---|---|---|---|
| Paper 107 | Bruce Nawrocki | Sending Email via SAS - An Introduction and More Advanced Concepts | |
| Paper 110 | Pratap Kunwar | Exploring the Power of Regular Expressions (RegEx) for Enhanced Pattern Matching and Replacement Operations in SAS® | |
| Paper 111 | David J. Bosak | Examining the R datastep() Function | |
| Paper 127 | Josh Horstman | Advanced DATA Step Look-Back and Look-Ahead Techniques | |
| Paper 131 | Josh Horstman | Fifteen Functions to Supercharge Your SAS® Code | |
| Paper 141 | Troy Martin Hughes | Five Reasons To Swipe Right on PROC FCMP, the SAS Function Compiler for Building Modular, Maintainable, Readable, Reusable, Flexible, Configurable User-Defined Functions and Subroutines | |
| Paper 17 | Hardik SHETH and Nathan Riley | Efficient Way to Create Adverse Event (AE) Summary Tables by System Organ Class (SOC) and Preferred Term (PT) using PROC SQL in SAS | |
| Paper 18 | Kiran Venna | Unanticipated Results: Uncovering Five Unexpected Outcomes in PROC SQL Joins | |
| Paper 43 | Pradip K. Muhuri, Charles Z.X. Han and John Vickery | One Click to Create a List of MEPS-HC ZIP Files' Download URLs: Web Scraping with SAS® and Python | |
| Paper 52 | Kennedy Kamau Gachigi | A SAS MACRO to parse clinical conductor timesheets | |
| Paper 53 | Richann Jean Watson and Louise S. Hadden | Just Stringing Along: FIND Your Way to Great User-Defined Functions | |
| Paper 57 | Michael A. Raithel | The SAS Data Set Missing Values Analyzer | |
| Paper 59 | Bruce Gilsen | Generating new variables from variable names and values in a DATA step | |
| Paper 61 | Michael A. Raithel | Using Formats to Subset Impossibly Large SAS Datasets | |
| Paper 63 | Jayanth Iyengar | Look Up Not Down: Advanced Table Lookups in Base SAS | |
| Paper 66 | Josh Horstman and Richann Jean Watson | From Muggles to Macros: Transfiguring Your SAS® Programs With Dynamic, Data-Driven Wizardry | |
| Paper 68 | Mark Jordan | A Brief Introduction to DS2 | |
| Paper 70 | Mark Jordan | A Fine Quotation: Macro Quoting in SAS | |
| Paper 72 | Piotr Krzystek | Utilizing SAS to Remedy Issues with Long Character Variables | |
| Paper 73 | GEONHYEOK JEONG and Ting Shi | Calculating the Comorbidity Index with user designed flexibility using SAS and R | |
| Paper 81 | David V. Cole | This Works on So Many Levels: Building Summary Tables in SAS® with Hash Programming | |
| Paper 84 | Jennifer Cano, Cainnear K. Hogan, Taylor N. Whittington, Julien Weinstein, Scott J. Denstaedt, Sarah M. Seelye and Hallie C. Prescott | SAS® Programming Methods to Identify Episodes of Hospital Readmissions | |
| Paper 98 | Kelly Smith and Elizabeth S. Davis | Automating Manual Processes with SAS: A Practical Example from Higher Education | |
| Paper 99 | Kelly Smith | From SAS to Power BI: Tips to Smooth the Way | |
| Statistics, Modeling, and Analytics (SMA) |
| Paper | Authors | Title | Key Takeaways |
|---|---|---|---|
| Paper 103 | Mostafa Zahed, Janet Kireta, Victor Aderanti and Maryam Skafyan | Unveiling the Diagnostic Potential of Radiomics-Based PET and CT Imaging through Statistical and Machine Learning Techniques | |
| Paper 116 | Danny R. Modlin | PROC BGLIMM The Smooth Transition to Bayesian Analysis | |
| Paper 118 | Danny R. Modlin | Customizing SAS Studio in Viya – The Next Step | |
| Paper 119 | Danny R. Modlin | Interactive Modeling with SAS Visual Statistics | |
| Paper 120 | Mira Shapiro, Lara Kassab, Jingyi Liu, Deanna Needell and Lorraine Johnson | Towards understanding Neurological manifestations of Lyme disease through a machine learning approach with updated patient registry data | |
| Paper 133 | HARISH PALAKURTHI | Comparative Analysis of Sentiment Prediction Methods Using Aspects in Consumer Reviews | |
| Paper 22 | Kirk Paul Lafler | Data Literacy 101: Understanding Data and the Extraction of Insights | |
| Paper 30 | Sandeep Srivatsav Gangaraju and Kiran Venna | Role of various factors impacting customer acquisition/ retention in a product-based organization using SAS®. | |
| Paper 33 | david horvath | Introduction to Developing Neural Nets | |
| Paper 38 | Ryan Paul Lafler and Anna Wade | Developing Artificial and Convolutional Neural Networks with Python's Keras API for TensorFlow | |
| Paper 39 | Ryan Paul Lafler | Charting Your Organization's Machine Learning Roadmap | |
| Paper 40 | Abbas Tavakoli, Pamela J. Wright and Robin M. Dawson | Determine the Dimensionality of Polycystic Ovary Syndrome Health-Related Quality of Life Questionnaire (PCOSQ-50) Using Factor Procedure and Parallel Analysis | Factor Procedure and Parallel Analysis |
| Determine the Dimensionality | |||
| Using SAS | |||
| Paper 42 | Melvin Alexander | Special Control Charts for Rare Events: G-charts and T-charts | |
| Paper 54 | Katharine Mary Scott | Leveraging SAS/IML for Audio Source Separation | |
| Paper 78 | Patralekha Bhattacharya | Deduping Time-Varying Redundant Transactions in Commercial Lending | |
| Paper 79 | Patralekha Bhattacharya | Building and Validating a Recommendation system using Python and SAS | |
| Paper 87 | Vindhya Hegde | Unlocking Insights: Harnessing Supervised Machine Learning for Advanced Data Analysis | |
| Paper 89 | Harshita Budumuru | Examining the Impact of ESG Performance on National GDP: A Comprehensive Analysis Using Sustainable Development Goals and SAS Tools | |
| Paper 91 | Tsung-hsun Tsai and Yung-chen Hsu | Using PROC PSMATCH for Assembling Parallel Test Forms | |
| Paper 93 | Valentina Grouverman and Lindzee Smith | SAS and Python can complement each other in statistical modeling | |
| Paper 95 | Brian Varney | An Introduction to Obtaining Test Statistics and P-Values from SAS® and R | |
| Visualization and Reporting (VR) |
| Paper | Authors | Title | Key Takeaways |
|---|---|---|---|
| Paper 113 | David J. Bosak | Replicating SAS® Reports in R: The REPORTER Package | |
| Paper 114 | Joshua J. Cook, Louise S. Hadden and Swann Arp Adams | Comparative Examples of Geocoding and Mapping Techniques in SAS® and R Using Centers for Disease Control Survey Data | |
| Paper 132 | Josh Horstman | Creating and Customizing High-Impact Excel Workbooks from SAS with ODS EXCEL | |
| Paper 140 | Chevell Parker | Next Level Reporting: ODS and Open Source | |
| Paper 35 | Imelda Go and Abbas Tavakoli | A SAS® Automation Example for Completing Reporting Templates in Microsoft® Excel | |
| Paper 41 | Abbas Tavakoli, Isabelle Renee Bennett and Robin B. Dail | Using SAS ® and R to Create Plot for Temperature Study | |
| Paper 44 | Kirk Paul Lafler and Joshua J. Cook | Building Amazing Dashboards Using SAS® Software | |
| Paper 45 | Kirk Paul Lafler | Ten Rules for Better Charts, Figures and Visuals | |
| Paper 47 | Zhengyang Shi | Streamlining Vaccine Study Output Generation with a Versatile Efficacy Macro | |
| Paper 55 | Raj Bhosale | Empowering Educational Institutions: Utilizing SAS® Tools for the Development of Student Retention Dashboard | |
| Paper 62 | Neeta v. Shenvi | Creating a Publication-Quality Multi-Panel Graph For Generalized Linear Model Analysis. | |
| Paper 85 | Cindy Zhang, Jessie Parker, Yeats Ye and Cordell Golden | Using Data Visualization to Explore the Association Between Education and Mortality: Insights from the 2012-2018 National Health Interview Survey Linked Mortality Files | |