MWSUG 2024 Papers - Index

63 papers. Source: MWSUG (Midwest SAS Users Group) conference proceedings.

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	%RepeatMaskoverFile	Paul Silver	Presentation	macro variable, &variable	source	12
2	%SEND_MSOFFICE_EMAIL - Pretty, Powerful, and Painless	Brooke Ellen Delgoffe			source	2
3	%SURVEYCANCORR Macro: Canonical Correlation for Complex Survey Data	Mark Pickering, Taylor Lewis, Raul Cruz	Classical canonical correlation analysis (CCA) is a statistical technique that is widely used when assessing associations between two sets of variables. Although methods exist in SAS for conducting basic CCA using `PROC CANCORR`, these methods are limited in their capacities to account for elements in complex survey design (CSD), and no methods have yet been developed that incorporate crucial CSD elements such as clusters or strata and provide meaningful results. Therefore, the accuracy of statistical significance assessments is limited when studying correlations involving datasets that contain CSD elements. To address this limitation, we developed the %SURVEYCANCORR macro. This paper outlines the theoretical basis of our macro and provides a comprehensive description of its options. Applying our macro to a national survey dataset demonstrates the different conclusions that can be drawn depending on whether CSD elements are accounted for when assessing the statistical significance of canonical correlations.	proc cancorr, correlation, proc calis	source	8
4	A General Review of Fuzzy Matching Techniques and Their Capabilities Using SAS	Kirk Paul Lafler	Data comes in all forms, shapes, sizes, complexities, and quality standards. Stored in text-based files, datasets, database systems, and other data structures, users across many industries recognize that data is often problematic and plagued with a variety of issues, including the absence of identifiers, or "keys", duplicate rows, and lack of standards to name a few. However, many data files will require "fine-tuning" the matching process, including the selection of multiple variables such as name, email and other social media addresses, telephone number, date of birth, street address, city, state, zip code, and social security number to ensure accurate and reliable matches or groups of similar values. This paper addresses the importance of working with reliable data sources, but when this is not possible, a comprehensive strategy of data cleaning, data validation, and data transformation processes are presented. We emphasize a sampling of data issues that can occur, popular data cleaning techniques, user-defined validation methods, data transformation approaches such as traditional merge and join techniques, the resolution of key identifier issues, the avoidance and control of false positives and negatives, the introduction of different SAS character-handling functions enabling phonetic and text string matching, including the SOUNDEX, SPEDIS, COMPLEV, and COMPGED functions, and an assortment and comparison of SAS programming techniques to successfully merge/join, match, and cluster less than perfect, or "messy" data	proc step, proc sql, character variable, proc sort, numeric variable, sas function, missing value, proc fastclus, proc format, data cleaning, date variable, order by, by group processing, type ii error, type i error, nominal variable, dataset, proc contents, proc print, character functions, proc transpose	source	31
5	A Quick Start Guide to Writing a Technical or Research Paper	Kirk Paul Lafler	Writing a technical or research paper is an essential skill needed by academics, researchers, scientists, and many professionals as it permits the effective communication of literature research, methodologies, and findings to others in your profession and to the world. So, how do you begin writing a technical or research paper? This quick start guide will provide step-by-step instructions, along with a few valuable tips and examples, to help you effectively navigate the writing of a technical or research paper. Regardless of whether you're already a skilled writer or just starting out, you will learn the essential elements and structure that is essential to effectively communicate your research and findings to others in a technical or research paper	bar chart, box plot	source	9
6	Adventures in Independent Consulting: Perspectives from Two Veteran Consultants Living the Dream	Joshua M. Horstman, Richann J. Watson	While many statisticians and programmers are content in a traditional employment setting, others yearn for the freedom and flexibility that come with being an independent consultant. In this paper, two seasoned consultants share their experiences going independent. Topics include the advantages and disadvantages of independent consulting, getting started, finding work, operating your business, and what it takes to succeed. Whether you're thinking of declaring your own independence or just interested in hearing stories from the trenches, you're sure to gain a new perspective on this exciting adventure	clinical trial, credit card	source	7
7	An Animated Guide to Proc Report Internals	Russell M. Lavery	`PROC REPORT` is an exciting "big file technique" that every programmer should know. `PROC REPORT` allows the creation of complicated reports, with many levels of summarization, while only reading a large source file one time. `PROC REPORT` allows, in just one step, summarization to multiple desired levels, calculation of new variables and the appending of different kinds reports into one complex report. ALL this happens in one read of the input data. Especially interesting is that the internal file for `PROC REPORT`, a file that holds "the combined multiple report" can be sent to a SAS data set and used as a input data. This 'static" paper attempts to show the time sequence of the internal actions of `PROC REPORT`. Knowing the time sequence of actions, especially calculations, is crucial to doing complicated `PROC` REPORTs. This paper supports a highly animated PowerPoint deck that is on the conference web site where the time sequence can be demonstrated as a sequence of events. This paper must use screen prints from the presentation and words, knowing words are less effective than visuals, to explain the time sequence. I suggest you use this paper, and the slides that are on the conference web site, and present this talk at your company as a lunch-and-learn. You can tell your company you learned this material at MWSUG and ask to be sent to future MWSUGs. I suggest you PLAY the slides (slide show- from current slide and hit the space bar to have the slide advance). The trick is to hit the space bar, wait for the motion to stop and describe what happened. All the code for the paper is in the appendix	proc report, proc step, sample, if-then-else, missing value	source	31
8	An Animated Guide to the Internals of GIT	Russell M. Lavery	This seminar is going to be an introduction to the internals of GIT. GIT lives on your computer and communicates with GIT-HUB, which lives on the web. For two reasons, this talk will not cover GITHub. First, learning GITHub, to some extent involves learning your company's way of working and this varies a great deal. Secondly, GIT itself is a very complicated topic. While I do not think using GIT has a positive ROI most situations, i do think it's worth learning, simply because the market wants people to know		source	1
9	An Introduction to Obtaining Test Statistics and P-Values from SAS and R	Brian K. Varney	Getting values of test statistics and p-values out of SAS and R is quite easy in each of the software packages but also quite different from each other. This paper intends to compare the SAS and R methods for obtaining these values from tests involving Chi-Square and Analysis of Variance such that they can be leveraged in tables, listings, and figures. This paper will include but not be limited to the following topics: * SAS `ODS TRACE`	output, anova, dataset, proc step, proc freq, output delivery system, ods output	source	9
10	Being A Statistical Expert Witness	David J. Corliss	Presentation	data science	source	21
11	Bessler's Principles of Communication-Effective Data Visualization	LeRoy Bessler	Presentation	time series analysis, bar chart, heatmap, vertical bar chart, bubble plot, index, class variable	source	125
12	Bessler's Recommendations for Using Color in Web Pages, Graphs, Tables, Maps, Text, and Print	LeRoy Bessler	Presentation	heatmap, excel, bubble plot, line chart, proc freq	source	76
13	Best Practices for Efficiency and Code Optimization in SAS programming	Jayanth Iyengar	There are multiple ways to measure efficiency in SAS programming; programmers' time, processing or execution time, memory, input/output (I/O) and storage space considerations. As data sets are growing larger in size, efficiency techniques play a larger and larger role in the programmers' toolkit. This need has been compounded further by the need to access and process data stored in the cloud, and due to the pandemic as programmers find themselves working remotely in distributed teams. As a criteria to evaluate code, efficiency has become as important as producing a clean log, or expected output. This paper explores best practices in efficiency from a processing standpoint, as well as others	proc step, dataset, proc append, proc sql, cpu time, if-then-else, sas log, where statement, data merging, sample, proc sort, sample size, sas programmer, lookup, data=, drop statement, missing value, index, options statement, character variable, hash table, numeric variable, set statement, sas base, key performance indicator, raw data, subsetting if	source	20
14	Bring Your SAS and Python Worlds Together With SASPy!	Ted Conway	Presentation	dataset, sas base, sample, standard deviation, bar chart, box plot, bubble plot	source	32
15	Building a SAS Data Viz Toolkit	Sara Richter	Presentation	proc step, proc report, output delivery system, user-defined format, line chart, missing value, proc sgplot, bar chart, regression analysis, macro variable, odds ratio	source	78
16	Calculating Cardinality Ratio in Two Steps	Ronald J. Fehd	The cardinality of a set is the number of elements in the set. The cardinality of a SAS software data set is the number of observations of the data set, n-obs. The cardinality of a variable in a data set is the number of distinct values (levels) of the variable, n-levels. The cardinality ratio of a variable is n-levels / n-obs; the range of this value is from 1/n-obs to one: (0-1]. Previous algorithms combined output data sets from the frequency and contents procedures in a `DATA STEP`. This algorithm reduces multiple frequency procedure steps to a single call, and uses scl functions to fetch contents information in the second `DATA STEP`. The output data set, a dimension table of the list of data set variable names, has variable cr-type whose values are in (few, many, unique, empty); this variable identifies the three main types of variables in a data set, few is discrete, many is continuous, unique is a row-identifier, and empty is a single value. The purpose of this paper is to provide a general-purpose program, ml-names-card-ratios.sas, which provides enhanced information about the variables in a data set. The author uses this list-processing program in Fast Data Review, Exploratory Data Analysis (EDA) and in Test-Driven Development (TDD), a discripline of Agile and Extreme Programming.	proc step, create table, macro variable, proc freq, dimension table, proc sql, out=, key	source	22
17	Charting Your Organization's Machine Learning Roadmap	Ryan Paul Lafler	Machine learning is experiencing a golden age of investment, democratization, and accessibility across all domains in the lif e sciences, natural sciences, and social sciences with applications to industry for business decision -making, risk management, consumer marketing, clinical trials, financial forecasting, security recognition, video remastering, digital twin simulations, and more. But what exactly is machine learning (ML)? How is it connected to Artificial Intelligence (AI)? And most importantly, how can data scientists, programmers, software engineers, and/or researchers start their endeavors into machine learning? This paper answers these questions, and more, by providing a roadmap to help navigate the complexities of machine learning in an application-oriented guide. This paper covers the main aspects of machine learning including supervised, unsupervised, and semi- supervised approaches as well as deep learning. The roadmap for supervised machine learning starts with linear regression and progressively builds towards more complex and flexible algorithms with discussions about the advantages and disadvantages of using certain models over others. This paper discusses the real-world applications of both labeled and unlabeled data; supervised and unsupervised machine learning algorithms; overfitting and underfitting; cross-validation; and the importance of hyperparameter tuning to better fit algorithms to their data	machine learning, regression analysis, neural network, deep learning, training set, artificial intelligence, data cleaning, proc reg, proc hpforest, model validation, mse, supervised learning, unsupervised learning, data mining, proc hpsplit, data science, proc princomp	source	7
18	Complex Custom Clinical Graphs Step by Step with SAS ODS Statistical Graphics	Richann J. Watson, Joshua M. Horstman	Presentation	bar chart, chi-square test, macro variable, proc sgplot, sas base, output delivery system, graph template language	source	44
19	Confessions of a PROC SQL Instructor	Charu Shankar	Presentation	proc sql, proc step, dictionary tables, select, cpu time, sas library, index	source	77
20	Create Report with SAS Visual Analytics to Finish Your SAS Story	Shinya Kodama	Presentation	data cleaning, data mapping, proc hpsplit, proc print, output delivery system, proc freq, proc sql	source	25
21	Creating and Customizing High-Impact Excel Workbooks from SAS with ODS EXCEL	Joshua M. Horstman	Love it or hate it, Microsoft `EXCEL` is used extensively throughout the business world. As a SAS user, you can enhance the impact of your work by using the `ODS` `EXCEL` destination to create high -quality, customized output in `EXCEL` format directly from SAS. This paper walks through a series of examples demonstrating the flexibility and power of this approach. In addition to complete control over visual attributes such as fonts, colors, and borders, the `ODS` `EXCEL` destination allows the SAS user to take advantage of `EXCEL` features such as multiple tabs, frozen or hidden rows and columns, and even `EXCEL` formulas to deliver the high-impact results you and your customers want!	excel, by group processing, proc step, xls, output delivery system, proc report, ods excel, proc template, macro variable, macro loop, do loop, sas output, macro definition, proc print	source	16
22	Dashboards Made Easy Using SAS Software	Kirk Paul Lafler	Organizations around the world develop business intelligence and analytics dashboards, sometimes referred to as enterprise dashboards, to display the status of "point-in-time" metrics and key performance indicators. Effectively designed dashboards extract real-time data from multiple sources for the purpose of highlighting important information, numbers, tables, statistics, metrics, performance scorecards and other essential content. This paper explores essential rules for "good" dashboard design, the metrics frequently used in dashboards, and the use of best practice programming techniques in the design of quick and easy dashboards using SAS software. Learn essential programming techniques to create real-world dashboards using Base- SAS software including `PROC SQL`, macro, Output Delivery System (`ODS`), `ODS HTML`, `ODS` `EXCEL`, `ODS` Layout, `ODS` Statistical Graphics, `PROC SGPLOT`, `PROC SGPIE`, and other technologies	proc step, proc report, excel, proc freq, proc sgplot, proc format, proc sort, proc univariate, title statement, end, proc template, order by, user-defined format, key performance indicator, output delivery system, business intelligence, proc contents, proc sql	source	42
23	Data Literacy 101: Understanding Data and Extracting Insights	Kirk Paul Lafler	Data is ubiquitous and growing at extraordinary rates, so a solid foundation with data essentials is needed. Topics include the fundamentals of data literacy, how to derive insights from data, and how data can help with decision - making tasks. Attendees learn about the types of data - nominal, ordinal, interval, and ratio; how to assess the quality of data; explore data using visualization techniques; and use selected statistical methods to describe data. Keywords: SAS, data, data literacy, understand data, structured data, unstructured data, nominal, ordinal, interval, ratio, data visualization, analytics, insights	dataset, data science	source	9
24	Developing Artificial and Convolutional Neural Networks with Python's Keras API for TensorFlow	Ryan Paul Lafler, Anna T. K. Wade	Capable of accepting and mapping complex relationships hidden within structured and unstructured data, neural networks are built from layers of neurons and activation functions that interact, preserve, and exchange information between layers to develop highly flexible and robust predictive models. Neural networks are versatile in their applications to real -world problems; capable of regression, classification, and generating entirely new data from existing data sources, neural networks are accelerating recent breakthroughs in Deep Learning methodologies. Given the recent advancements in graphical processing unit (GPU) `CARDS`, cloud computing, and the availability of interpretable APIs like the Keras interface for TensorFlow, neural networks are rapidly moving from development to deployment in industries ranging from finance, healthcare, climatology, video streaming, business analytics, and marketing given their versatility in modeling complex problems using structured, semi -structured, and unstructured data. This paper explores fundamental concepts associated with neural networks including their inner workings, their differences from traditional machine learning algorithms, and their capabilities in supervised, unsupervised, and generative AI workflows. It also serves as an intuitive, example-oriented guide for developing Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) architectures using Python's Keras and TensorFlow libraries for regression and image classification tasks	neural network, data cleaning, regression analysis, training set, unstructured data, deep learning, response variable, mae, machine learning, missing value, feature extraction, validation set, proc reg, structured data	source	18
25	Enhancing Your SAS Viya Workflows with Python: Integrating Python's Open-Source Libraries with SAS using PROC PYTHON	Ryan Paul Lafler, Miguel Bravo Martinez del Valle	Data scientists, statistical programmers, machine learning engineers, and researchers are increasingly leveraging a growing number of open-source tools, libraries, and programming languages that can enhance and seamlessly integrate with their existing data workflows. One of these integrations, built into SAS Viya, is its pre-configured Python runtime integration, `PROC PYTHON`, that gives SAS programmers access to Python's open -source data science libraries for wrangling and modeling structured and unstructured data alongside the validated procedures provided in SAS. This paper demonstrates how to install and import external Python libraries into their SAS Viya sessions; generate Python scripts containing methods that can import, process, visualize, and analyze data; and execute those Python methods and scripts using SAS Viya's `PYTHON` procedure. By integrating the added functionalities of Python's libraries for data processing and modeling with SAS p rocedures, SAS programmers can enhance their existing data workflows with Python's open-source data solutions	dataset, proc python, proc step, sas viya, macro variable, sas log, proc contents, libname statement, sas output, sas programmer, work library, data wrangling, data pipeline, raw data	source	11
26	Evaluating Internal Consistency of Questionnaire Data	Yida Bao, Zheng Zhang, Jiafeng Ye, Liangliang Xu, Tao Jiang, Yongli Zhao, Philippe Gaillard	In the field of questionnaire data analysis, evaluating the internal consistency of scales is crucial for ensuring the reliability of measurement instruments. This paper explores the use of SAS for analyzing questionnaire data, focusing on methods that assess internal consistency and reliability. It presents the application of several statistical techniques, including Split-Half Reliability, Cronbach's Alpha, and McDonald's Omega, using both simulated and real datasets. The primary objective is not merely to evaluate these reliability measures but to illustrate how to utilize SAS effectively for conducting reliability analysis in questionnaire research. By demonstrating the implementation of these methods in SAS, this paper aims to provide researchers with a practical guide for assessing questionnaire quality, where the focus on methodology supersedes the importance of results.	correlation, factor loading, factor analysis, data science, sample, proc step, macro variable, proc corr, proc sgplot	source	15
27	Existence is Fruitful: How to determine whether something exists in your SAS session	Erin O'Dea	Presentation	macro variable, macro definition, filename statement, proc sql, proc step, character constant, macro function	source	12
28	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, proc step, automatic macro variable, macro facility, macro loop	source	52
29	From Paper To Pedigrees: A Beginners Approach with SAS	Vijay Susmitha Bommini	Hospital paper records data differ significantly from Electronic Health Records (EHR) data in terms of their format, structure, maintenance, and accessibility. Paper records are typically unstructured, and manually entered which often leads to errors in the entry of information and makes it difficult to obtain complete and accurate data. This paper explores how SAS software was effectively used to transform these unstructured hospital paper records into a structured format as part of the "Utility of Family Data Extracted from Hospital Records" project at Marshfield Clinic Research Institute. This involved a series of steps, from abstracting the paper records data into REDCap electronic form followed by comprehensive data conversion, cleaning, and transformation steps utilizing SAS. As a beginner in SAS, I utilized the SAS Enterprise Guide, focusing on the `SQL` Procedure, DATA steps, and conditional logic to manage data extraction, cleaning, and transformation. The goal was to create actionable and reliable datasets from hospital paper records information compliant with the Family Mapping Algorithm (FMA) (Huang, et al., 2021), which establishes familial relationships using demographic and	proc sql, proc step, union all, create table, data merging, select, input statement, if-then-else, extract	source	14
30	Harnessing the Power of PROC CONTENTS and SELECT INTO: SEPARATED BY	Yanfang Pang	Presentation	proc contents, label statement, study day, macro variable, proc step, adverse events, pd	source	17
31	How to build custom macros in SAS: A step-by-step approach to translating your code into a custom macro	Sara Richter	Presentation	macro variable, proc tabulate, proc step, macro definition	source	60
32	How to Land Work as a SAS Professional	Charu Shankar	By 2025, the U.S. is expected to face a shortage of around 250,000 to 500,000 workers with deep analytical and data science expertise. This shortage is driven by the rapid increase in demand for roles like data analysts, data scientists, and machine learning engineers across various sectors, including finance, healthcare, and technology. According to reports and studies by organizations such as McKinsey & Company, there is a predicted 30-40% gap between the demand for data science workers vs the supply. The shortage isn't limited to highly specialized data roles. There is also an expected gap of up to 1.5 million managers and analysts who need to be data-savvy to interpret and act on big data analysis. These individuals may not be deep technical experts but need to be able to work with data-driven insights to make strategic decisions.	sas programming, sample	source	24
33	Introduction to Machine Learning in SAS	Melodie Rush	Presentation	machine learning, gradient boosting, proc hpsplit, neural network, proc hpforest, sas viya, supervised learning	source	71
34	Inventory your OS for Programming Information	Brian K. Varney	Whether you are attempting to figure out what you have when preparing for a migration or you just want to find out which files or directories are taking up all of your space, SAS is a great tool to inventory and report on the files on your desktop or server. This paper intends to present SAS code to inventory and report on the location you want to inventory	proc sql, dataset, proc step, sas enterprise guide, macro definition	source	8
35	Just Stringing Along: FIND Your Way to Great User-Defined Functions	Richann J. Watson, Louise S. Hadden	SAS provides a vast number of functions and subroutines (sometimes referred to as CALL routines). These useful scripts are an integral part of the programmer's toolbox, regardless of the programming language. Sometimes, however, pre-written functions are not a perfect match for what needs to be done, or for the platform that required work is being performed upon. Luckily, SAS has provided a solution in the form of the `FCMP` procedure, which allows SAS practitioners to design and execute User -Defined Functions (UDFs). This paper presents two case studies for which the character or string functions SAS provides were insufficient for work requirements and goals and demonstrate the design process for custom functions and how to achieve the desired results	proc step, proc fcmp, proc catalog, dictionary tables, user-defined format, format statement, sas function, do loop, proc format, data cleaning, proc sql	source	10
36	List Processing using SQL Select Into to Replace Call Symputx Creating Indexed Arrays of Macro Variables	Ronald J. Fehd	SAS software provides the ability to allocate a macro variable in a `DATA STEP` with its call symputx routine. This routine can be used to make a sequentially-numbered series of macro variables mvar1-mvarN which is refered to as an indexed array of macro variables. The purpose of this paper is to examine the algorithm of macro variable array usage and provide sql consolidations of the various tasks.	proc sql, macro variable, call symputx, proc step, character functions, macro put, out=, proc freq, proc sort, macro definition	source	13
37	Looking for the Missing(ness) Piece	Louise S. Hadden	Reporting on missing and/or non-response data is of paramount importance when working with longitudinal surveillance, laboratory, and medical record data. Reshaping the data over time to produce such statistics is a tried-and-true technique, but for a quick initial look at data files for problem areas, there is an easier way. This quick tip will speed up your data cleaning reconnaissance and help you find your missing(ness) piece. Additional tips on making true missingness easy to identify are included in this paper	proc step, proc freq, missing value, proc report, proc univariate	source	14
38	Making Text Analytics More Approachable: From Traditional to Generative AI for Business Applications	Vikranth Reddy Chapaala	Presentation	data science, probability distribution, deep learning, output delivery system, sample	source	13
39	Making the Most of User-defined Formats	Sara Richter	Presentation	proc step, proc format	source	29
40	NHANES Dietary Supplement component: a parallel programming project	Jayanth Iyengar	The National Health and Nutrition Examination Survey (NHANES) contains many sections and components which report on and assess the nation's health status. A team of IT specialists and computer systems analysts handle data processing, quality control, and quality assurance for the survey. The most complex section of NHANES is dietary supplements, from which five publicly released data sets are derived. Because of its complexity, the Dietary Supplements section is assigned to two SAS programmers who are responsible for completing the project independently. This paper reviews the process for producing the Dietary Supplements section of NHANES, a parallel programming project,	dataset, proc step, proc sql, sample, create table, proc compare, quality assurance, if-then-else	source	20
41	Operator, Please? Making the Most of SAS Language Operators	Joshua M. Horstman	Presentation	missing value, where statement	source	8
42	Predicting the Impact of Storms on Utility Customers	David J. Corliss	Presentation	data science, neural network, proc hpforest, artificial intelligence, feature importance	source	23
43	Q&A with the macro maven: is sql our lingua franca?	Ronald J. Fehd	SAS software provides an implementation of Structured Query Language (sql). Usage of the `SQL` procedure creates an opportunity for the user to learn database terminology and consider how to address their programming problems and reporting as a matter of the design of the data structure. The purpose of this Q&A dialogue is to review the suitability of sql as a common language for users and programmers. Its benefits include a knowledge of design of data structure for ease of reporting as well as the ability to research the SAS global symbol table. The dialogue is an exposition of how we learn both natural and artificial languages, and how we use them to communicate.	proc sql, dimension table	source	9
44	Revelation and Exploration for COVID-19 Data: Visual Data Insights and an InfoGeographic Atlas	LeRoy Bessler	Presentation	bar chart, response variable, scatter plot, correlation, excel, macro facility, missing value	source	97
45	SAS Job Searching and Interviewing tips - Strategies in the Post-Pandemic era	Jayanth Iyengar	Searching for work in the data analytics market is more competitive than ever before. Part of this is due to the nature of the work environment, which has shifted from in office, on-site to remote and hybrid positions. Also, because of the emergence and growth of open-source tools, such as R and Python, candidates for SAS positions need to be familiar with these coding tools. In this paper, I will outline tips and strategies for success in the application and interview process in the post-pandemic era. I will also discuss the interview process in detail, from initial HR interviews to final interviews with a hiring manager	sample, proc step, sas programmer, proc sql	source	17
46	SAS PROC GEOCODE By Example: A Case Study	Louise S. Hadden	Presentation	proc step, sas base, binary variable, create table, order by, data science	source	44
47	SAS Programming And Generic Techniques for Cohort Creation and Consort Diagrams	Brandy R. Sinco		proc sql, create table	source	4
48	Simplify Complex SAS Logic through the Power of Bit Level Data Comparison	Kent Phelps, Ronda Phelps	Business requirements are built on a foundation of business rules that guide the business logic in your SAS programs. Business logic often becomes more complex than it needs to be ~ making it challenging to design simple and efficient programs. Our presentation highlights a powerful data transformation and analysis tool called Bit Level Data Comparison. This tool was designed in SAS and other languages and successfully used in a variety of industries. Join us for a great opportunity to learn how to replace long lines of repetitive complex business logic with simple concatenations and SELECT WHEN statements. You will be empowered to break down and map business logic into Bit Levels and groups of lags, codes, values, and indicators in various combinations. This session on Bit Level Data Comparison will expand your toolkit and enable you to design programs that are more enjoyable and easier to code, read, update, and maintain.	data validation rules, data transformation	source	18
49	Soft Skills to Gain a Competitive Edge in the 21st Century Job Market	Kirk Paul Lafler	The 21st-century economy is converging with existing technologies like proprietary and open-source software, the Internet, robotics, and the Cloud; with emerging technologies like artificial intelligence (AI), machine learning (ML), IoT, nanotechnology, and biotechnology. And to add to all this is the collection, curation, and processing of vast quantities of data and information - all converging at breakneck speeds resulting in the blurring of boundaries between humans and machines. The 21st century job market is experiencing a technological revolution that is changing faster than ever, and organizations, along with today's workforce, must learn how to cope and adapt. Keywords: 21st century, job market, hard skills, soft skills, communication, critical thinking, leadership, teamwork, work ethic, attitude		source	4
50	Stacking Up: Horizontal or Vertical with PROC SQL or DATA Step	Charu Shankar	Presentation	proc sql, proc step, proc append, data merging, set statement, inner join, data step	source	59
51	Teaching Your Computer to See: Using Computer Vision to Detect Defects	Scott Koval	Presentation	sas viya, deep learning, proc freq, heatmap, response variable, neural network, machine learning	source	23
52	The Business Value of Diversity Combined with Data Science	Stephen B. Sloan	Presentation	data science, sample	source	14
53	The Everytown Research database: Using SAS analytic procedures to analyze mass shootings	Jayanth Iyengar	With mass shootings occurring every week, it can accurately be stated that mass shootings in the U.S. have reached the level of an epidemic. Everytown Research and Policy conducts independent methodically rigorous research and supports evidence-based policies to reduce the incidence of gun violence. In 2009, Everytown Research started assembling a Mass Shooting database which records key data on every mass shooting in the U.S. In this paper, I examine and explore the database using SAS procedures to produce a series of tables, reports, graphics and visualizations. The goal of this project is to generate insights from the SAS analytics that guide the building of effective programs and policies to reduce the epidemic of mass shootings	cpu time, proc step, proc freq, if-then-else, proc sgplot, dataset, proc contents, proc import, proc sql, excel, sas base, sas output, sas studio, character variable, proc print, proc report, index, numeric variable, proc means	source	31
54	The Premium Pricing Dilemma	Sai Suresh Marreddy, Kusuma Lanka	Presentation	customer retention, churn prediction, attrition, customer experience, sensitivity analysis	source	20
55	Using LaTeX document class sugconf to write your paper	Ronald J. Fehd	SAS software conferences now accepts papers produced by the LATEX document preparation system which produces a .pdf. This paper illustrates use of the LaTeX document class sugconf, shows a basic paper template and provides references to basic and advanced usage of LaTeX.	macro definition, macro variable	source	17
56	Using SAS I/O Functions to Retrieve External Metadata	Jake Reeser	This paper discusses external file and I/O functions. I cover how to use these functions to retrieve SAS metadata efficiently, external files, as well as what metadata is available to the user. I will show how to access the metadata of all files in an external file directory. I will lastly show how to use this file metadata to allow for easier data processing	dataset, sas log, macro function, macro variable	source	8
57	Using SAS to Consume Data from APIs	Somaiya Shakil, Jack N. Shoemaker	The world is moving to the cloud and that means using APIs to consume and publish data. At the simplest level, an API is just a web address that you could use a command-line tool like cURL to access. What comes back is often a JSON data payload. SAS provides several tools which make access easier and more robust. Using the public CMS Beneficiary Claims Data API (BCDA), this paper will show how to use a combination of command-line tools, `PROC HTTP`, and `PROC JSON` to communicate with the CMS API. The BCDA returns data in newline-delimited JSON (NDJSON) which presents a few more wrinkles to iron out. NDJSON files contain one or more JSON payloads. `PROC JSON` operates on a single payload, so needs some help to consume NDJSON-formatted data. This paper will present two solutions to this challenge as well as a complete package that authenticates and consumes the CMS data	libname engine, proc step, macro variable, proc http	source	6
58	Using SAS To Manage And Maintain Your Data Repository	Jose Centeno	Presentation	proc sql, proc step, create view, proc append, raw data, length statement, alter table	source	16
59	Using SAS/STAT: A Gentle Introduction to Some Frequently Used Tools	Melodie Rush	Presentation	sas enterprise guide, scatter plot, proc step, sample, chi-square test, correlation, proc freq	source	123
60	Vaping Trends: Analyzing Patterns and Predictive Factors in Electronic Cigarette Use among American Youth	Medha Haldar, Carol Antony, Rishanki Jain	Electronic cigarettes or e-cigarettes, designed originally as a less harmful alternative to traditional tobacco products for addicts, have become increasingly popular among American adolescents, raising substantial public health concerns. This study aims to explore the prevalence, patterns, and health implications of e- cigarette use among youth in America by analyzing data from the National Youth Tobacco Survey (NYTS). The research employs both descriptive and predictive analytical methods to uncover trends and factors influencing e-cigarette use. Our descriptive analysis reveals a high percentage of high school students who use e-cigarettes, with significant influences from ease of access, peer pressure, and social media marketing. The study also identifies a concerning overlap between e-cigarette use and the use of other tobacco products. Predictive modeling highlights critical factors such as social media exposure, misperceptions of e-cigarette safety, and environmental influences that contribute to e-cigarette consumption. The findings underscore the effectiveness of the current regulatory measures and pinpoint gaps in enforcement. Recommendations include implementing stricter advertising and sales restrictions, enhancing public education on the risks of e-cigarettes, and improving regulatory practices to limit youth access. Despite existing regulations, the persistence of e-cigarette use among adolescents indicates ongoing challenges that require a comprehensive approach involving educators, policymakers, and public health professionals to safeguard youth health and prevent addiction	normalization, proc means	source	12
61	Visual Analytics 101: From ETL to Dashboard	Charu Shankar, Marty Hultgren	Presentation	sas viya, cas, bar chart, proc means, proc step, sas 9, vertical bar chart	source	90
62	What if Proc Power Can't Help? Calculating Power via Simulation, Example with Count Regression	Brandy R. Sinco, Joceline Vu, Pasithorn Suwanabol	Background: Reviewers for an article comparing opioid use after surgery questioned the amount of power for a Zero-Inflated Negative Binomial (ZINB) model. Power was estimated by simulation because `PROC POWER` does not have a ZINB option. Objectives. For a given dataset, show how to use Procs GenMod or CountReg to estimate the distribution parameters for Poisson, Negative Binomial, ZIP, and ZINB models. Then, use the streaminit() and RAND() functions to simulate distributions in a data stop. Run the outcomes model on the simulated data and compute the power. Methods. Begin with one simulation. After calculating the ZINB parameters with `PROC GENMOD`, compare the simulated distribution to the actual data with graphics and descriptive statistic from Procs Freq and Univariate. Next, simulate 1,000 datasets. Put an index on _REPLICATION_ to maximize execution speed. Run the outcomes procedure 1,000 times and compute the simulation-based power as the percentage of times when the outcome is significant. I.E., compute power based on the definition of the probability of observing a significant difference between treatment and control, given that a significant difference is present in the data. To calculate power for the model with covariates, use bootstrapping. Compare the actual data to the simulated data with Procs Freq, Means, T-Test, and NPar1Way. Results. After outputting the results of `PROC GENMOD` by _REPLICATION_ into an `ODS` dataset, the original dataset with 562 patients had 78% power to detect a significant difference in opioid use between surgical procedures without adjustment for additional covariates	proc step, binomial distribution, proc power, poisson distribution, proc genmod, regression analysis, negative binomial regression, zero-inflated negative binomial, proc freq, sample, sample size, logistic regression, proc mixed, logistic distribution, monte carlo, poisson regression	source	16
63	What's Your Favorite Color? Controlling the Appearance of a Graph	Richann J. Watson	The appearance of a graph produced by the Graph Template Language (GTL) is controlled by Output Delivery System (`ODS`) style elements. These elements include fonts and line and marker properties as well as colors. A number of procedures, including the Statistical Graphics (SG) procedures, produce graphics using a specific `ODS` style template. This paper provides a very basic background of the different style templates and the elements associated with the style templates. However, sometimes the default style associated with a particular destination does not produce the desired appearance. Instead of using the def ault style, you can control which style is used by indicating the desired style on the `ODS` destination statement. However, sometimes not a single one of the 50-plus styles provided by SAS achieves the desired look. Luckily, you can modify an `ODS` style template to meet your own needs. One such style modification is to control which colors are used in the graph. Different approaches to modifying a style template to specify colors used are discussed in depth in this paper	bar chart, proc template, proc step, sample, series plot, output delivery system	source	33

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