WUSS 2024 Papers - Index

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

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	A hands-on introduction to end-to-end data projects in Python for SAS Programmers	Matthew T. Slaughter, Isaiah Lankham	Presentation	sas programmer, dataset	source	3
2	A SAS Macro to Calculate Therapeutic Intensity Score	Rachelle Juan	Hypertension is the leading cause of cardiovascular disease and premature death globally. Therapeutic inertia has been identified as a key barrier to effective hypertension control. Evaluation of therapeutic intensification by calculating the therapeutic intensity score (TIS) can be helpful to raise awareness, monitor, and properly manage uncontrolled blood pressure in a timely manner. Despite the importance of TIS in hypertension treatment, its use is limited due to the lack of standardized programs or tools for TIS calculation. This paper presents a systematic approach and a simple SAS program demonstrating the data structure and programming process for calculating the TIS and defining therapeutic intensification of hypertension medications. To demonstrate our methods, we used pharmacy data from the electronic health records of Kaiser Permanente Southern California.	in=, sample, numeric variable	source	9
3	A Survey of Some of the Most Useful SAS Functions	Ronald P. Cody	Presentation: There are so many useful SAS functions it is very difficult to choose which ones to discuss. Several of the functions in this talk are relatively new and amazing. They can save enormous amounts of time. This talk covers some of my favorite functions.	missing value, character variable, length statement, lag function, sas log, call sortn, yrdif	source	61
4	Advanced DATA Step Look-Back and Look-Ahead Techniques	Joshua M. Horstman	The `DATA STEP` is a powerful and versatile tool for manipulating SAS data sets. Because it is built around the concept of reading one record at a time into the Program Data Vector, `DATA STEP` programming logic generally has access to only one record at a time. However, there are many situations in which it is advantageous to be able to look back at values from prior records or to look ahead at values from later records. This presentation introduces several advanced `DATA STEP` programming techniques that allow the programmer to work with values from multiple records concurrently. Approaches discussed include the double `SET` statement, the self-merge, the LAG function, and more. Examples are used to explain each method and demonstrate its use.	set statement, lag function, in=, merge statement, pdv, by group processing, missing value, if-then-else, moving average	source	5
5	Airbnb Consumer Behavior Analysis	Evelyn Larson, Anabeli Chandra	"Oh, the places you'll go !" In the dynamic landscape of business, understanding the customer is key. This research delves into the intricate tapestry of Airbnb user trends to predict whether a customer will book their stay with Airbnb after creating an account. This study aims to improve Airbnb conversion rates by optimizing marketing strategies, implementing predictive modeling, and optimizing customer conversions. The methodology of our research included a multifaceted approach to extract meaningful patterns from the vast dataset. JMP was used to partition the data into a manageable subset useful in analysis. We then utilized JMP to preprocess, cleanse, and transform the data, ensuring its reliability and relevancy. Predictive analysis via SAS Model Studio was used to create numerous predictive models to determine whether customers will book.	neural network, proc hpsplit, gradient boosting, logistic regression, likelihood function, logistic distribution, regression analysis, data cleaning	source	14
6	An introduction to Quarto: A Versatile Open-Source Tool for Data Reporting and Visualization	Joshua J. Cook, Swann Arp Adams	In the collaborative landscape of data analysis, a common frustration among analysts stems from the need to integrate and harmonize different programming languages within a team. Teams often comprise interdisciplinary researchers, each with their unique programming preferences and expertise, leading to complexities in project integration and continuity. The difficulty in compiling and executing data projects cohesively can hinder efficiency and impede the delivery of coherent, multi-faceted data insights. This challenge necessitates a solution that can bridge the gaps between varying coding languages and methodologies to streamline team collaboration and project completion. Addressing this issue, the point of this paper is to present Quarto as an innovative solution that can unify the diverse programming approaches within a team. Quarto stands out by offering extensive cross- language support, enabling the integration of code from multiple languages into a singular, dynamic report.	data science, primary endpoint	source	18
7	Analyzing Goalkeeper Impact in Premier League Football	Alec Troy Christiansen, Benjamin T Houser	The sport of football (US "soccer") contains untapped potential in the world of data analytics. The sport's main objective is scoring goals, and one position plays a pivotal role in whether goals are scored or not. The goalkeeper role is widely considered one of the most difficult positions to play in football. Knowing the key aspects of a goalkeeper's performance is vital to understanding the success of a club. This study seeks to quantify the importance of a goalkeeper's ability to perform aspects of his role as it holistically impacts the team's performance within the context of the English Premier League. The analysis will utilize data from Sports Reference's Premier League raw data on goalkeeper statistics for the five most recently completed seasons (2018-2023) as well as wins, losses, and draws recorded by clubs. We conducted stepwise regression techniques to identify key variables and construct a model based on the results with the lowest AIC values.	regression analysis, correlation, proc reg, cook's distance, predictor variable, stepwise selection, sample, missing value, histogram, excel	source	10
8	Array Hashing: Simple, Fast, and Efficient	Paul M. Dorfman	In SAS programming, using hashing for memory-resident storage and lookup began in 1998 with array implementations of hash algorithms in the SAS language. They vastly outperformed other same-purpose SAS methods available at the time, and so SAS programmers began to include them in their repertoires. However, with the advent of the SAS hash object in 2003, array hashing started falling into obscurity. One, valid, reason is the true power of the hash object. The other is the proliferation of the baseless notion that array-based hash code is too complex to comprehend and maintain. This is quite unfortunate: Under many scenarios, array-based hash search is simpler, much faster and more efficient than the hash object. This paper presents the simplest (and yet most practical) hash search algorithm and its array-based SAS implementation as plain modifications of the sequential search.	hash table, composite key, sample, do loop, lookup, data wrangling, array, sas programmer, sas programming, hashing function	source	12
9	Automation of Submission of Programs in Analysis Data Reviewer's Guide for FDA and PMDA Submissions	Junying Zhang	Analysis Data Reviewer's Guide (ADRG) is recommended as an important part of a standards -compliant analysis data submission for clinical trials. ADRG Section 7 itemizes the programs that are included in the submission. This paper presents three useful SAS macros to automatically create Section 7 Submission of Programs for ADRG. MACRO1 automatically creates 7.1 ADaM Programs, MACRO2 creates 7.2 Analysis Output Programs and MACRO3 creates 7.3 Macro Programs. Linux command, Perl Regular Expression and SAS functions are used to extract the related information. The three macros have an option to create the tables according to FDA and PMDA requirements and add common customization options per different study requirements.	in=, sample, adam dataset, dataset, character functions, pd, ods pdf, sas function, sdtm domain, sas log, macro definition, cdisc	source	14
10	Benefits, Challenges, and Opportunities with SAS and Open-Source Software (OSS) Integration	Kirk Paul Lafler, Ryan Paul Lafler, Joshua J. Cook, Stephen B. Sloan, Anna T. K. Wade	Presentation	proc sql, relational database, data visualization, machine learning, data science, sql server, data mining	source	33
11	Beyond Macro - Data-Driven Programming with CAS in SAS Viya	Mark L. Jordan	With the adoption of SAS Viya accelerating processing in CAS is becoming more common, and CAS speaks CASL. Seasoned SAS coders often use SAS macro to produce data-driven programs, automating tedious programming tasks. When working with CAS from the SAS Compute Server, it's important to know where, when, and how the CASL that executes is generated. Whatever your experience level, the interactions between SAS code, CASL, and Macro can be intimidating. This presentation aims to demystify that process.	proc sql, cas action, macro facility, do over, libname engine, proc step, macro trigger, macro definition, cas, sql pass-through	source	27
12	Can You Teach An Old Dog New Tricks?	Scott Burroughs	It's a fact of life that things change, and that also applies to our jobs, regardless of industry. With our jobs being technical, things will likely change even faster. It may or may not seem surprising that SAS is still the main software used in this industry, but when a 'free' alternative comes out, companies are going to consider it. Additionally, our jobs may change (to a new company or within our companies) and so software platforms are likely to change. As a self-professed old-timer, can I learn these new ways of doing things after having done them other ways (maybe even one way) for so long?	sas programming, sas enterprise guide	source	2
13	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? 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 giving attendees a roadmap to help them 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.	machine learning, regression analysis, neural network, deep learning, training set, artificial intelligence, data science, data cleaning, unsupervised learning, proc reg, proc hpforest, model validation, data mining, proc hpsplit, proc princomp	source	6
14	Commit early, commit often! A gentle, hands-on introduction to the joy of Git and GitHub	Isaiah Lankham, Matthew T. Slaughter	Presentation		source	3
15	Comparative Examples of Geocoding and Mapping Techniques in SAS and R Using Centers for Disease Control Survey Data	Joshua J. Cook, Louise S. Hadden, Swann Arp Adams	The Centers for Disease Control (CDC) maintains the Behavioral Risk Factor Surveillance System (BRFSS), which is a rich source of survey data for public health and epidemiological research. BRFSS collects data in all 50 states as well as the District of Columbia and three U.S. territories, and includes data on health-related risk behaviors, chronic health conditions, and use of preventive services. Geospatial analysis is a crucial tool in public health and epidemiology, offering insights into geographic patterns and trends. This presentation aims to provide a comprehensive comparison of geocoding and mapping techniques using SAS and R, utilizing data from the CDC BRFSS. By demonstrating how to geocode and visually map public health data, we seek to empower researchers and practitioners with the skills necessary to conduct spatial analyses.	proc step, sas base, lookup	source	14
16	Correlation of a Composite Self-Reported Symptoms Severity Score with Patient Number of Symptoms as Predictors of SARS-CoV-2 infection status/Viral Load using the SAS System	Jesse Albert Canchola, Daniel Jarem, Alison Kuchta	Presentation	correlation, generalized estimating equations, data science, akaike information criterion, response variable, likelihood function, clinical significance	source	54
17	Count, Lag, Retain! Let's go through it one more time!	Steven C. Black	In SAS there are few things that can turn a good programmer into a pale, sleep deprived, shadow seeking individual like a RETAIN statement that is not working right. In this paper I hope to provide a deeper understanding how to better count on and count with the RETAIN statement in SAS. I will illustrate a number of ways to count using the properties of the program data vector and provide some real-life examples of how I have found using the RETAIN statement super helpful. Coupled with these two options I'll also touch on the LAG function. In the end I hope to bring out of the shadows and into the light the RETAIN, SUM and LAG functions.	retain, lag function, pdv, by group processing	source	10
18	Creating Custom Excel Spreadsheets with Built-in Autofilters Using SAS Output Delivery System (ODS)	Kirk Paul Lafler	Spreadsheets have become the most popular and successful data tool ever conceived. Current estimates show that there are more than 750 million `EXCEL` users worldwide. A spreadsheet's simplicity and ease of use are two reasons for the growth and widespread use of `EXCEL` around the globe. Additional value-added features have also helped to expand the spreadsheet usefulness among a growing number of users including its collaborative capabilities, being customizable, ability to manipulate data, application of data visualization techniques, mobile device usage, automation of repetitive tasks, integration with other software, data analysis, and filtering capabilities using autofilters. This last value-added feature, filtering with autofilters, is the theme for this paper.	excel, proc report, proc step, output delivery system	source	6
19	CTRL + S(HARE): A Programmer's Advice for Branding on Social Media	Inka Leprince	Navigating social media as a programmer requires a blend of technical knowledge, personal branding, and community engagement. The foci of this presentation are the best social media practices and strategies for enhancing visibility through tagging individuals/companies, establishing regular posting schedules, and leveraging fun and interactive content. Resources for free design software will be provided as well as practical tips for integrating branding elements in the creation of visually striking graphics. By following these guidelines, you can effectively promote yourself and engage your followers.	service provider, proc freq	source	5
20	Customized LOG/LST name with the SAS Config File	Zeke Torres	The SAS config file is powerful and useful. In this example, we show how to customize the names of the Log and List files from the code we run, and obtain a useful new name as the	macro facility, macro definition, proc printto, proc step	source	11
21	Customizing SAS Studio in Viya - The Next Step	Danny R. Modlin	Presentation	sas studio, data science	source	5
22	Deciding What to Write About in the SAS World	Jane Eslinger	Presentation	proc report, proc step, sas programming	source	4
23	Developing a SAS Macro for ISNI: Assessing Missing Data Sensitivity Beyond MAR Assumptions	Bocheng Jing, L. Grisell Diaz-Ramirez, W. John Boscardin	Missing data is a common challenge in research, often addressed by statistical methods assuming that the data are Missing At Random (MAR). While this assumption simplifies analysis, it is unverifiable and can introduce bias if incorrect. To address this, s ensitivity analyses assess the robustness of conclusions to deviations from MAR. The Index of Local Sensitivity to Nonignorability (ISNI), introduced by Troxel et al. (2004) for cross -sectional data and extended by Xie et al. for longitudinal data, provide s a straightforward method for this purpose without requiring complex non -MAR models. Currently, the ISNI method is available in the R package "isni" for various regression models, featuring user -friendly syntax. However, a corresponding SAS procedure has yet to be developed. To fill this gap, we present a SAS macro, %isni, for calculating the ISNI index.	missing value, sensitivity analysis, proc step, regression analysis, likelihood function, logistic regression, logistic distribution, estimate, p-value, proc sql, standard deviation, proc calis, covariance, create table, maximum likelihood estimation, proc mixed	source	16
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.	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	Estimating causal effects of health interventions using instrumental variables	Mehrnaz Siavoshi	Consistent estimation of causal effects is pivotal in health research to inform effective policy-making and treatment strategies. This paper presents a brief exploration of instrumental variables (IVs) as a methodological approach for estimating causal effects when randomization is impractical or not feasible, such as when utilizing observational data. In this technique, traditionally employed in the fields of epidemiology and econometrics, a third endogenous variable is incorporated in model development to understand the causal relationship between two endogenous variables, while allowing for controls and other traditional modeling techniques.	instrumental variables, regression analysis, treatment effect, residual, predictor variable, proc step, causal inference	source	7
26	Fifty Shades of SAS Programming, or 53 (+3) Syntax Snippets for a Table Look-up Task, or How to Learn SAS by Solving Only One Exercise!	Bartosz Jabłoński, Quentin McMullen	It is said that a good programmer should be lazy. And what about a good programming teacher? We dare to say the same is true. This article will show that you can be lazy and also be able to teach an entire SAS course at the same time! The aim of the article is to present a variety of examples of how to do one of the most common data processing programming tasks, table look-up, in SAS. We don't assess these methods from a bench- marking or performance perspective but rather present them as an intellectual puzzle. Our goal is to explore how much SAS syntax (statements, PROCs, functions, etc.) could be taught using only one exercise. If you are a fan of unorthodox SAS programming, curious to learn about the variety and flexibility of the SAS language, or an innovative SAS teacher - this article is for you!	proc step, macro variable, proc sql, big data, set statement, hash table, proc sort, proc transpose, dataset, call execute, create table, if-then-else, txt, data step, proc append, proc print, where statement, macro loop, macro put, proc format, proc iml, proc tabulate, %if-%then-%else, macro definition, by group processing, cas action, index, merge statement, proc fcmp, proc lua, sas packages	source	50
27	Five Easy Ways to Make Your SAS Code Run Faster	Mark L. Jordan	Presentation: Don't you just hate waiting for your results after submitting a SAS program? I sure do! In this session, I'll demonstrate five easy "thumb rules" that you can easily use to help you write faster-running SAS code. These techniques are valid for code executed in SAS 9 or the SAS Viya Compute Server. I'll demonstrate head-to-head comparisons of the different techniques in SAS 9, using SAS datasets and Oracle tables as data sources. You'll see how simple coding choices affect execution time, and learn which choices provide the best performance in the circumstances we commonly encounter every day.	sas 9, proc sql	source	10
28	Flight Delay Analysis and Prediction	Eric C Braga, Miguel Ángel Bravo Martínez del Valle, Riley Rutan, Fernanda Carrillo	Like most global industries, the COVID-19 pandemic caused fundamental shifts in the airline industry. As a result, the consumer is often left with the inconvenience of delayed and canceled flights for various reasons. In this project, we explored a large dataset of canceled and delayed flights from 2019-2023 from the US from the Department of Transportation along with weather data from NOAA, leveraged machine learning models to identify trends in delayed and canceled flights, and created a tool that consumers can use to predict the probability that their upcoming flight will be delayed or canceled. After using several machine learning models, we utilized an Extreme Gradient Boosting (XGBoost) model that achieved an accuracy of 0.68 and a weighted average f1-score of 0.70.	proc hpsplit, logistic distribution, neural network, proc hpforest	source	19
29	From Keys to Credit: A Deep Dive into Homeownership's Influence on Loan Quality	Yi-Che Chen, Debanik Chakraborty	In banking, analysts strive to identify traits predicting loan delinquency. Homeownership status, reflecting stability and financial behavior, poses a crucial question: "Does homeownership significantly influence loan delinquency?" The objectives of this study were twofold: building a predictive model leveraging borrower attributes to assess loan quality and interpreting homeownership's impact. To achieve this, we aimed to combine real-world public data sources with the loan dataset collected from Kaggle, which contains 27 attributes 396,030 individual. We reviewed the data and performed data manipulation to create new variables. To address imbalanced data, we applied SMOTE sampling technique to train our models and tested on the original data to check the practical implication of MLR models. Six classification prediction models (Logistic Regression, Decision Trees, SVM, Neural Networks, Random Forests, Gradient Boosting) were implemented. These models were evaluated using Accuracy, F-1 Score, ROC-AUC score, KS (Youden) and Misclassification Rate. The final model aims to inform credit assessment, providing insights into homeownership's role in loan quality. Logistic Regression proves to be the most accurate model with the highest ROC-AUC (71.46%), F-1 Score (43.43%) and one of the lowest False Negative Rate (31.64%). We found total interest rate, term of the loan, debt to income ratio, income verification status and revolving credit utilization rate to be the most important features. Our model suggests that the chance of delinquency is more for the applicants without a mortgage house, though it has no impact on the applicant having a mortgage house.	logistic regression, logistic distribution, regression analysis, interest rate, type ii error, confusion matrix, binary variable, default, gradient boosting, nominal variable, response variable, training set, machine learning, proc hpforest, random sampling, sample, likelihood function	source	15
30	From Muggles to Macros: Transfiguring Your SAS Programs With Dynamic, Data-Driven Wizardry	Joshua M. Horstman, Richann J. Watson	Presentation: Static 'muggle' code is full of hardcodes and data dependencies. It is not flexible, breaking easily when unexpected inputs or conditions are present; difficult to maintain, requiring modifications when data or environment changes; and difficult to reuse. Macro language 'magic' can eliminate these problems by making code dynamic, data-driven, and reusable. This presentation covers a macro language review, and applying macro magic to data values, metadata, and environmental data.	macro variable, proc sql, proc step, macro facility, variable list, macro loop, %if-%then-%else	source	52
31	From Word Clouds to Phrase Clouds to Amaze Clouds: A Data-Driven Python Programming Solution To Building Configurable Taxonomies That Standardize, Categorize, and Visualize Phrase Frequency	Troy Martin Hughes	Word clouds visualize the relative frequencies of words in some body of text, such as a website, white paper, blog, or book. They are useful in identifying contextual focus and keywords; however, word clouds- as commonly defined and implemented -suffer numerous limitations. First, multi -word phrases such as "data set" or "Base SAS" are unfortunately segmented into single words-"data," "set," "Base," and "SAS." Second, desired capitalization often cannot be specified, such as visualizing "`PROC PRINT`" even when its lowercase "`PROC PRINT`" is observed in text or code. Third, spelling variations (e.g., single and plural nouns, various verb conjugations, abbreviations and acronyms) are not mapped to each other. Similarly, and fourth, comparable words or phrases (e.g., "`PROC PRINT`" and " `PRINT` procedure") are not mapped to each other, representing a further lack of entity resolution.	proc freq, proc fcmp, proc step, sas function, entity resolution, macro put, data step, dataset, proc print, relative frequency	source	12
32	Get a GPS to Navigate Your Skills to Find Career Purpose	Lisa A. Mendez, Charu Shankar	Presentation	sas programming, sas base, sas enterprise guide, data science, proc sql, sas viya, proc step	source	11
33	GIT Intro for SAS Coders that Need to GIT Started	Zeke Torres	With the advent of more team diversity in skills syntax, the challenges have only increased in how to integrate new team members, code styles, code upgrades and syntax. Even if you are a single person creating and editing your own code, its time to Git started. This is the how to start if you don't know how to Git started, especially if you are working with a team. The need exists for teams to have a practical outline of elements incorporated to achieve a strategic harmony of SAS, Python, R (and more) with Git or some version control. Improve code transparency version control with useful team code review methods. On top of all that, we must manage the data we use with our code. It's important to document a projects key decision points during an analytics cycle. Doing so increases the accuracy of analytics results by reducing issues due to missing key code documentation.		source	7
34	Handling Missing Values in SAS 9 and SAS Viya	Melodie Rush	Presentation	missing value, proc step, imputation, proc freq, proc sql, multiple imputation, proc mi	source	102
35	Hands-on Python PDFs: Using the pypdf Library To Programmatically Design, Complete, Read, and Extract Data from PDF Forms Having Digital Signatures	Troy Martin Hughes	The pypdf Python library ( https://pypdf.readthedocs.io/en/stable/index.html) facilitates the programmatic creation, completion, cropping, and merging of PDF forms. Form data-including both dynamic text and field values-can be programmatically written to a PDF using pypdf, and data manually entered into PDF form fields by users can be programmatically extracted . With this combination of functionality, pypdf is a powerful tool that can build dynamically generated PDF forms that simplify user completion of forms, as well as subsequent form validation. This text introduces users to the pypdf library, and demonstrates a single use case in which copyright grant forms (CGFs , aka copyright -release or permission -to-publish forms) were automatically generated for the Western Users of SAS Software (WUSS) 2024 conference proceedings.	data validation rules, data cleaning	source	22
36	Here Comes the Rain (Cloud Plot) Again	Bartosz Jabłoński	Rain cloud plots are very popular and practical tools for data visualization. They enable the display of several components in one figure, including: kernel density estimates, data points, and box-and- whisker plots. This method facilitates the comparison of variable distribution across different cate- gories. During this presentation, a macro called %RainCloudPlot() will be introduced and its capabili- ties for creating rain cloud plots will be presented. Sample code will accompany all examples.	class variable, density plot, box plot, sas packages	source	21
37	How does SAS Viya and Open Source Integrate	Melodie Rush	Presentation	sas viya, supervised learning, proc step, cas action, proc python, sas studio, machine learning	source	66
38	How RANK are your deciles? Using PROC RANK and PROC MEANS to create deciles based on observations and numeric values	Lisa A. Mendez	For many cases using `PROC RANK` to create deciles works sufficiently, but occasionally, you find that it does not work for your needs. `PROC RANK` uses number of observations to produce a rank; however, if you need weighted percentiles then `PROC RANK` will not work. Instead, you can use `PROC MEANS` to successfully create weighted percent groups. This paper will illustrate the basic usage of `PROC RANK` and how to use `PROC MEANS` for the alternative. The paper will utilize BASE SAS 9.4 code and will use a fictional dataset that provides the total number of prescriptions written by providers for two years. All levels of SAS users may benefit from the information provided in this paper.	proc step, proc rank, out=, frequency analysis, proc freq, numeric variable, proc sql	source	9
39	How to Modify SAS 9 Programs to Run in SAS Viya	Danny R. Modlin	Presentation: This session describes how to modify existing SAS 9 programs to run in SAS Viya. It covers SAS Viya servers and processing environments, including the CAS server and the compute server, and the considerations for moving SAS 9 code to the SAS Viya platform.	sas viya, proc step	source	4
40	iCSR: A Wormhole to Interactive Data Exploration Universe	Sudhir Kedare, Steve Wade	In the past decade, CDISC has brought standardization to clinical data, significantly benefiting clinical development by allowing regulatory reviewers to review submitted data with efficiency and precision. Despite these advances, bottlenecks such as static TFL report submission hinder the overall efficiency of the review cycle. At Jazz, we are continuously seeking innovative ways to explore data to establish a foundation for digital transformation. Namely, we have developed the Interactive Clinical Study Reports (iCSR) application, utilizing CDISC-standardized data and RShiny capabilities to enable interactive data exploration. This paper demonstrates how iCSR facilitates real-time dynamic data exploration and significantly improves the data review process by providing fast insights and enabling informed decision-making. iCSR comes with an intuitive UI and minimal onboarding.	proc means, clinical trial, data science, cdisc	source	7
41	Identifying a Skilled Nursing Facility Associated COVID-19 Case in Surveillance Data	Kathryn Doh, Maggie G Tufts	Skilled Nursing Facilities (SNFs) posed a unique public health concern during the COVID-19 pandemic, having both an elevated risk of transmission, as a setting where individuals lived and worked with limited ability to isolate, and an elevated risk for severe disease and death, as a setting serving medically vulnerable individuals. To provide as accurate as possible SNF COVID-19 case and death counts, we developed a series of macros to identify cases likely occurring in SNF residents or staff using a combination of free-text search, cross-walked selection fields, and address matching. Results from this effort were used to validate facility reported data, get demographic data, and identify cases not linked to an outbreak that likely should be.	sample, quality control	source	15
42	Incorporating Macro with PROC REPORT Code	Jane Eslinger	`PROC REPORT` is used across many industries to generate reports that often need to be generated on a weekly, monthly, or quarterly basis. The `PROC REPORT` code for these reports must be robust enough to handle things like new data, different dates, and changes to titles and headers. Busy programmers don't have time to update the program for every new run, but smart programmers know that SAS Macro must be utilized. This paper explores how to incorporate SAS Macro into `PROC REPORT` code, going beyond simple macro variable references. Through examples, you will learn how to write code that both iterates over a series of macro variables and parses a singular macro variab le.	proc report, proc step, macro variable, macro definition	source	11
43	inspectoR: QC in R? No Problem!	Steve Wade, Sudhir Kedare	More organizations are starting to embrace open-source technologies to perform tasks traditionally completed in SAS. One such activity is to QC datasets, tables, and figures in the process of producing TLF's. Independent programming is done for many of those TLF's, comparing the results from both programmers. Jazz has developed the {inspectoR} package, an alternative to `SAS/COMPARE` procedure, to allow QC performed in R to be compared back to datasets coming from a SAS system. In this paper, we demonstrate how {inspectoR} will compare these datasets and produce a report showing the findings. The report produced by {inspectoR} is much like `PROC COMPARE` output but is produced in HTML in a more readable format. {inspectoR} have proven to be a valuable tool in helping to transition QC tasks to R and maintain the level of quality expected from SAS systems.	ods html, independent qc, proc compare	source	7
44	Integration of an R Language Function Within SAS to Perform Conditional Multivariate Saddlepoint Approximation	Christopher Johnson	Issues with small and sparse data are well known to produce bias in maximum likelihood estimates (MLEs) when performing logistic regression analyses. A common alternative to ordinary maximum likelihood based logistic regression is exact logistic regression, whereby an exact distribution of sufficient statistics for one or more parameters of interest is generated, conditional upon the sufficient statistics for one or more nuisance parameters. Through this generated exact conditional distribution, conditional maximum likelihood estimation may be performed to obtain accurate parameter estimates and conduct hypothesis testing. A drawback of this approach is that parameters are estimated fully conditionally when more than one parameter of interest exists. That is, each parameter of interest is estimated separately and completely conditional on all other parameters of interest.	logistic regression, regression analysis, logistic distribution, likelihood function, estimate, maximum likelihood estimation, confidence interval, hypothesis testing, proc calis, out=, monte carlo, proc step, proc logistic, r programming	source	8
45	Interactive Data Analysis and Exploration with composR: See the Forest AND the Trees	Steve Wade, Sudhir Kedare	Deciding on post-hoc analyses can be a time-consuming process and it is critical an analysis has been vetted prior to production and release. Jazz is pioneering creative techniques in data discovery using open-source technologies. Accordingly, composR is an interactive R-Shiny application that provides a user-friendly interface for rapid data analysis and exploration. This paper demonstrates how composR allows users to filter data, create new variables, and perform analyses, summaries, and visualizations. It also enables further drill-down into summaries and charts for more in-depth analysis. Although not required, composR supports CDISC standard data from a variety of file types including SAS, XPT, `CSV`, `EXCEL`, and Rda. ComposR reduces time, effort, and cost over traditional TLF processes by providing insights into analyses prior to formal TLF production. This ensures only necessary analyses are produced, eliminating much of the back-and-forth between departments.	sample, nominal variable, cdisc	source	6
46	It's All About the Base-Procedures	Jane Eslinger	As a Base SAS programmer, you spend your day manipulating data and creating reports. You know there is a procedure that can give you what you want. As a matter of fact, there is probably more than one procedure to accomplish the task. Which one should you use? How do you remember which procedure is best for which task? This paper is all about the Base procedures. It explores the strengths of the commonly used, nongraphing procedures. It discusses the challenges of using each procedure and compares it to other procedures that accomplish similar tasks. The first section of the paper looks at utility procedures that gather and structure data: APPEND, COMPARE, CONTENTS, DATASETS, FORMAT, SORT, SQL, and TRANSPOSE. The next section discusses the Base SAS procedures that work with statistics: FREQ, MEANS/SUMMARY, and UNIVARIATE. The final section provides information about reporting procedures: PRINT, REPORT, and TABULATE.	proc step, proc sql, proc contents, proc freq, proc report, proc tabulate, proc compare, proc transpose, proc append, proc print, proc format, proc sort, sas base, proc datasets, proc univariate, create table, dictionary tables, macro variable, proc means	source	20
47	Jedi SAS Tricks for DS2 Programmers	Mark L. Jordan	If you've heard about the SAS DS2 programming language, you probably know that it combines the process control of the `DATA STEP` with the simplicity and power of SQL. You may even know about DS2's simple syntax for parallel processing and ability to handle most ANSI data types at full precision. But there are many lesser-known superpowers built into the DS2 language that you can leverage.	ods csv, parallel processing	source	10
48	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
49	LAST CALL to Get Tipsy with SAS: Tips for Using CALL Subroutines	Lisa A. Mendez, Richann J. Watson	This paper provides an overview of six SAS CALL subroutines that are frequently used by SAS programmers but are less well-known than SAS functions. The six CALL subroutines are CALL MISSING, CALL SYMPUTX, CALL SCAN, CALL SORTC/SORTN, CALL PRXCHANGE, and CALL EXECUTE. Instead of using multiple IF-THEN statements, the CALL MISSING subroutine can be used to quickly set multiple variables of various data types to missing. CALL SYMPUTX creates a macro variable that is either local or global in scope. CALL SCAN looks for the nth word in a string. CALL SORTC/SORTN is used to sort a list of values within a variable. CALL PRXCHANGE can redact text, and CALL EXECUTE lets SAS write your code based on the data. This paper will explain how those six CALL subroutines work in practice and how they can be used to improve your SAS programming skills.	sample, macro variable, call execute, symbol table, call symputx, cpu time, call sortc, local symbol table, date variable, call symput, missing value, numeric variable, call missing, proc sort, macro call, proc step, character variable, sas log, macro put, proc sql	source	20
50	Leading with Impact: Building Stronger Programming Teams Through Effective Leadership and Collaboration	Menaga Guruswamy Ponnupandy	In the fast -paced environment of Contract Research Organizations (CROs), programming teams are essential for crafting innovative solutions and ensuring operational efficiency . However, these teams often face challenges that hinder productivity and job satisfaction, including issues with resource allocation, overtime management, and conflict resolution. This white paper investigates these challenges and proposes practical solutions to enhance a cooperative and dynamic work environment . By focusing on enhancing professional development, adopting incl usive leadership practices, managing workloads effectively, and mitigating micromanagement, the paper aims to help CROs build a culture of collaboration and empowerment. The paper is structured around ten detailed case studies, each providing specific scenarios and solutions designed to enhance team performance, increase employee retention, and drive sustainable growth and innovation.	resource allocation, independent qc	source	6
51	Limit of Detection Calculation Methods Comparison for PCR-based Quantitative Studies using the SAS System	Jesse Albert Canchola	In assay performance evaluation for quantitative assay studies based on polymerase chain reaction (PCR), the Limit of Detection (LoD) is defined as the lowest concentration or amount of analyte that is consistently detectable (in our case, in at least 95% of the samples tested; CLSI EP17-A2). In practice, the estimation of the LoD uses a parametric curve fit to a set of panel member (PM1, PM2, PM3, etc.) data where the responses are binary (i.e., percent detected at a certain level). Typically, the parametric curve fit to the percent detection levels takes on the form of a logistic or probit distribution. Additional methods for estimation of the LoD in PCR-based studies include using the "based on hit rate" and maximum likelihood estimation (MLE) methods (Singh & Nocerino, 2001). Amongst all methods, the MLE method is preferred since the MLE is taken to be sufficient (Rice, 1988) given the selected parent probability distribution assumed to model the data.	sample, macro definition, logistic distribution, maximum likelihood estimation, likelihood function, confidence interval, sample size, poisson distribution, probability distribution	source	13
52	Macro Variable Arrays Made Easy with macroArray SAS package	Bartosz Jabłoński	A macro variable array is a jargon term for a list of macro variables with a common prefix and nu- merical suffixes. Macro arrays are valued by advanced SAS○Rprogrammers and often used as "driving" lists, allowing sequential metadata for complex or iterative programs. Use of macro arrays requires ad- vanced macro programming techniques based on indirect reference (aka, using multiple ampersands &&), which may intimidate less experienced programmers. The aim of the paper is to introduce the macroArray SAS package. The package facilitates a solution that makes creation and work with macro arrays much easier . It also provides a "DATA-step-arrays-like" interface that allows use of macro arrays without complications that arise from indirect referencing. Also, the concept of a macro dictionary is presented, and all concepts are demonstrated through use cases and examples.	macro variable, macro definition, sas packages, macro variable array	source	49
53	Mastering Clinical Trial Reporting with SAS	Matthew J. Becker	Presentation	life sciences, clinical trial, data governance	source	4
54	Matrices and More: An Overview of the SAS/IML Language	Rick Wicklin	Presentation	proc princomp, proc iml, dataset, maximum likelihood estimation, monte carlo, proc step, regression analysis	source	46
55	Multiple Imputation in Interim Analyses	Bill Coar	Presentation	imputation, missing value, multiple imputation, proc reg, logistic regression, regression analysis, proc step	source	29
56	Navigating Career Crossroads: From Statistical Manager to Entrepreneur	Iuliana Constantin	Losing my position as Statistical Manager in November 2023 due to a restructuring process was a moment of reflection and transformation. Although the decision was unrelated to my professional performance and driven by cost-cutting measures, it prompted me to think critically about my career and what I really wanted to do. Since I have been working as a Statistical Programmer for almost 16 years, I enjoyed analyzing data, and working with my team on the current studies, but was the time for me to do something else? This paper is about my personal experience of being laid off and embracing entrepreneurship. Throughout the narrative, I offer insights and strategies for navigating the transition from employee to small business owner, considering both the positive and negative effects of being an entrepreneur. I hope through out this white paper, I can offer useful lessons for others considering a similar leap.	pharmaceutical industry, clinical trial, excel	source	8
57	PROC BGLIMM: The Smooth Transition to Bayesian Analysis	Danny R. Modlin	Presentation	covariance, proc step, proc mixed, covariance structure, sample, group by, fixed effects	source	20
58	PROC SORT (then and) NOW	Derek P. Morgan	The `SORT` procedure has been an integral part of SAS since its creation. The sort-in-place paradigm made the most of the limited resources at the time, and almost every SAS program had at least one `PROC SORT` in it. The biggest options at the time were to use something other than the IBM procedure SYNCSORT as the sorting algorithm, or whether you were sorting ASCII data versus EBCDIC data. These days, `PROC SORT` has fallen out of favor; after all, `PROC SQL` enables merging without using `PROC SORT` first, while the performance advantages of HASH sorting cannot be overstated. This leads to the question: Is the `SORT` procedure still relevant to any other than the SAS novice or the terminally stubborn who refuse to HASH? The answer is a surprisingly clear "yes". `PROC SORT` has been enhanced to accommodate twenty-first century needs, and this paper discusses those enhancements.	proc sort, sample, proc step, proc sql, out=, sas programmer, order by, options statement	source	11
59	Qualitative and Quantitative Data Analysis and Visualization Using Python	Leon Rod Davoody, Lida Gharibvand	This paper explores the capabilities of Python for qualitative and quantitative data analysis and visualization. From data processing to visualization techniques, Python offers a versatile toolkit for researchers and analysts working with diverse datasets. It uses bar charts and pie charts to visually represent qualitative insights as well as histograms and box plots for quantitative insights. The paper delves into practical examples to showcase the application of Python's ecosystem of libraries and demonstrate its utility in research and decision-making processes. The results demonstrate that Python's rich ecosystem is a powerful tool for qualitative and quantitative analysis as well as data visualization.	box plot, bar chart, histogram	source	6
60	Regression Analysis Made Easy Using SAS Studio	Zheyuan (Walter) Yu	SAS OnDemand for Academics (ODA) provides students, faculty, and SAS learners with free access to SAS software and the SAS Studio user interface using a web browser. SAS Studio provides a comprehensive and customizable integrated development environment (IDE) for all SAS users. To showcase SAS Studio's many features, numerous techniques will be introduced to access, clean, transform, analyze, and visualize data using the point-and-click features found in SAS Studio's Navigation Pane's Tasks and Utilities. Plus, we'll demonstrate the generated SAS code that is automatically produced from the point-and-click techniques. To obtain a high-level understanding of the datasets being used, we'll demonstrate tasks associated with exploratory data analysis (EDA) to identify missing values, explore outliers, and evaluate trends in the data.	regression analysis, proc reg, predictor variable, sas studio, dataset, correlation, logistic regression, response variable, multiple regression, proc step, simple linear regression, logistic distribution, odds ratio, sas library, confidence interval, data transformation, interval variable, scatter plot, proc freq, outlier, missing value, proc sql, proc means, excel	source	42
61	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	16
62	SAS Macro Programming Tips and Techniques	Kirk Paul Lafler	The SAS Macro Language is a powerful tool for extending the capabilities of the SAS System. This hands-on workshop teaches essential macro coding concepts, techniques, tips and tricks to help beginning users learn the basics of how the Macro language works. Using a collection of proven Macro Language coding techniques, attendees learn how to write and process macro statements and parameters; replace text strings with macro (symbolic) variables; generate SAS code using macro techniques; manipulate macro variable values with macro functions; create and use global and local macro variables; construct simple arithmetic and logical expressions; interface the macro language with the `SQL` procedure; store and reuse macros; troubleshoot and debug macros; and develop efficient and portable macro language code.	macro variable, macro facility, proc step, autocall macro, macro definition, macro parameter, %macro parameters, macro function, proc sql, dictionary tables, options statement, macro loop, &variable	source	10
63	SAS Studio, SAS Enterprise Guide, SAS Extension for Visual Studio Code: Which should I use?	Casey Smith	Presentation	sas enterprise guide, data wrangling, sas 9, sas studio, sas programming	source	16
64	SAS Visual Analytics a low code no code approach to dashboard reporting	Charu Shankar	Presentation	bar chart, sas visual analytics, sas viya, data visualization, data analyst, data science, correlation	source	56
65	SAS Viya in Action: Enhancing Life Sciences Data Analysis	Matthew J. Becker	Presentation	life sciences, clinical trial, clinical data, sas viya, proc hpsplit, data science, data governance	source	13
66	SAS Viya vs SAS 9: Architecture and Performance For SAS Users / Teaming With Your SAS Architect and Admin For A Fast SAS Viya	Michael Shealy	Presentation	sas 9, sas viya	source	13
67	Simulating Optimal Sample Sizes for Joyful Canine Jaws Using SAS	Chary Akmyradov, Lida Gharibvand	In the realm of veterinary clinical research, ensuring animal welfare while achieving statistically significant results is paramount. This study presents an advanced approach to sample size calculation for a clinical study involving canine subjects, with a focus on dental health. The unique design of this study employs dogs as both cases and controls by longitudinally comparing treated and untreated teeth within the same animal, thus minimizing the number of subjects required and reducing animal suffering. A pivotal aspect of this research is the optimization of the number of dogs and the number of teeth extracted per dog. The goal is to minimize both, ensuring minimal discomfort to participating animals. The teeth growth in dogs is monitored at three distinct time points to assess the development of treated versus untreated teeth. To achieve a robust and reliable study design, a simulation-based approach was adopted.	sample, proc step, sample size, proc freq, proc glimmix, null hypothesis, power analysis, beta distribution, macro definition	source	11
68	Simulation in SAS for Estimating Power	Michael Williams	Presentation	proc step, sample, type i error, null hypothesis, probability distribution, proc iml, proc power	source	18
69	Statistical Programmers Role for SPM Analysis	Marckenley Mercie, Ce (Colin) Zhou	In the context of multiple myeloma (MM) clinical trials, analyzing secondary primary malignancies (SPM) is crucial. Regulatory obligations require thorough investigation and reporting of SPM occurrences. However, a lack of consensus on executing this analysis leads to industry variations. This paper outlines a concise process for creating analysis datasets and offers recommendations for standardizing SPM reports to support this critical analysis. It also emphasizes the essential role of statistical programmers in conducting these analyses effectively.	statistical programmer, sample, adam dataset, create table, clinical trial, order by, proc step, flag variable, proc freq, adverse events, cdisc	source	11
70	Streamlining Your Workflow: Creating Portable and Automated SAS Project Folders Using Your SAS Enterprise Guide Project Name	Chary Akmyradov	Presentation	sas enterprise guide project, libname statement, sas enterprise guide, permanent library, sas function	source	14
71	Taking the Mystery Out of and Debugging PROC HTTP	Kim M. Wilson	Several great papers have been written about how to get started with `PROC HTTP`, which includes accessing Microsoft 365 applications, modifying various options for desired results, and more. As a SAS Technical Support Engineer, I often assist SAS customers who are not receiving the expected resource, or they are seeing a return code that is not a 200 OK. This paper describes common errors that you might encounter regarding certificates, authentication, and general errors, as well as overall debugging techniques and suggestions. This paper also helps you gather pertinent information that SAS Technical Support will need when helping to solve the problems occurring with or around `PROC HTTP`.	proc http, proc step, sysrc, sas viya, error message, sas log, rest api, options statement, proc options	source	10
72	Ten Tips for Simulating Data with SAS	Rick Wicklin	Presentation	sample, proc step, rand, monte carlo, regression analysis, by group processing, sample size	source	34
73	The Business Value of Diversity Combined with Data Science	Stephen B. Sloan	Having strong diversity programs, being sensitive to diversity issues, and being able to apply rigorous data science techniques can provide significant value to an organization. Many business problems and challenges begin with the statement of a problem, and the problem sometimes seems to relate to issues with diversity. Many organizations are aware of both the importance of diversity and the fact that problems sometimes manifest themselves as diversity issues, even when they have other causes. At other times there are issues where awareness of the value of diversity can help an organization achieve its goals. In addition, there are areas where diversity can have a direct impact on the organization in terms of compliance and scientific value. In this paper I cite examples where diversity has proven to have business value for an organization.	data science, clinical research	source	5
74	The Current State of Teaching Biostatistics in Academia	Lida Gharibvand	Presentation	sample, power analysis, data science, descriptive analytics, feature selection, sample size	source	14
75	The Essentials of SAS Dates and Times	Derek P. Morgan	The first thing you need to know is that SAS software stores dates and times as numbers. However, this is not the only thing you need to know. This presentation gives you a solid base for working with dates and times in SAS. It introduces you to functions and features that enable you to manipulate your dates and times with surprising flexibility. This paper shows you some of the possible pitfalls with dates (and times and datetimes) in your SAS code and how to avoid them. We show you how SAS handles dates and times through examples, including the ISO 8601 formats and informats and how to use dates and times in TITLE and FOOTNOTE statements. The paper closes with a brief discussion of `EXCEL` conversions.	sysdate9, date variable, sample, excel, macro variable, proc format, proc step, missing value, error message, user-defined format, character variable, input statement, numeric variable, time, elapsed time, time., macro function	source	23
76	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
77	The SAS Supervisor	Donald J. Henderson	How SAS processes jobs is the responsibility of the SAS Supervisor, and an understanding of its function is important. While the details of how it works have changed over time, much of the basics of the SAS Supervisor have been reasonably consistent over time.	dataset, set statement, by group processing, keep statement, drop statement, merge statement, proc step, output, execution phase, do loop, sas programmer, automatic variable, update statement	source	13
78	Time Since Last Dose: Anatomy of a SQL Query	Derek P. Morgan	Even though much of what we need to accomplish as SAS programmers can be accomplished using the `DATA STEP`, SQL can provide an alternative to large amounts of data manipulation. First, this paper will walk you through how SQL joins two datasets. Second, we will walk through the case of determining time since last (or first) dose and present an SQL solution in place of multiple SORTs, MERGEs, TRANSPOSEs, and use of the LAG function. Not only is the code economical, but the execution is also economical. This may also help to increase your understanding of how the WHERE statement, and the SQL GROUP BY, and HAVING clauses work.	adverse events, proc sql, sample, where statement, group by, proc step, create table, order by	source	12
79	Training Domain Specific AI for Clinical Data: Developing an AI Agent to Transform EDC Data to CDISC SDTM and then to ADaM Standards	Sy J. Truong	The transformation of clinical trials data from Electronic Data Capture (EDC) systems into CDISC- compliant formats, such as the Study Data Tabulation Model (SDTM) and the Analysis Data Model (ADaM) is resource-intensive, requiring significant manual effort and expertise. Automating this transformation with domain-specific AI models can provide efficiencies and reduces errors. This paper explores the development and training of an AI model, utilizing SAS as the primary scripting language, complemented by Python for model training and integration with open-source models such as Llama and OpenAI's latest models.	macro definition, training set, sas log, clinical data, clinical research, data integrity, clinical trial, data transformation, artificial intelligence, data science, cdisc	source	12
80	Uniform Hashing of Arbitrary Input Into Key-Exclusive Segments	Paul M. Dorfman, Donald J. Henderson	Aggregating or combining large data volumes can challenge computing resources. For example, the process may be hindered by the system limits on utility space or memory and, as a result, either fail or run too long to be useful. It is a natural inclination to try solving the problem by segregating the input records into a number of smaller segments, processing them independently and combining the results. However, in order for such a divide-and-conquer tactic to work, two seemingly contradictory criteria must be met: First, to aggregate or combine the data correctly, no segment can share its key values with the rest; and second, the segments must be more or less equal in size. In this presentation, we show how a hash function can be used to achieve it for arbitrary input with no prior knowledge of the distribution of the key values among its records.	composite key, create table, proc sql, sample, hash table, proc step, data wrangling, where statement, proc freq	source	17
81	Using VS Code with SAS	Chris Hemedinger	Presentation	sas programming, sas enterprise guide, sas programmer, sas viya	source	4
82	What comes after the semicolon? How I learned to stop worrying and love the meeting	Isaiah Lankham, Matthew T. Slaughter	Presentation		source	3
83	Who's Still Bringing That Big Data Energy? Leading a SAS Conference by Leveraging a 48-Year Longitudinal Analysis of 30,498 Presentations in the SAS User Community	Troy Martin Hughes	This analysis examines presentations at SAS user group conferences between 1976 and 202 4, and supplements the introductory analysis that the author debuted at WUSS 2023 . (Hughes, 2023) Both analyses evaluate white papers maintained on www.LexJansen.com (aka "the LEX") , as well as other presentations referenced on this inimitable site. Presentations are drawn from multiple conferences, including: SAS User Group International (SUGI, may she rest in peace), SAS Global Forum (SGF, may she be revived), SAS Explore, Western Users of SAS Software (WUSS), Midwest SAS Users Group (MWSUG), South Central SAS Users Group (SCSUG), Southeast SAS Users Group (SESUG), Northeast SAS Users Group (NESUG), Pacific Northwest SAS Users Group (PNWSUG), and Pharmaceutic al Software Users Group (PharmaSUG). This current analysis evaluates the critical role that the 2023 analysis played in data- driven decision-making for the WUSS 2024 conference.	big data, extract	source	5
84	Will statistical programmers be replaced by Artificial Intelligence?	Junying Zhang	Presentation	statistical programmer, data science, artificial intelligence, crf, clinical trial, raw data, regulatory submission	source	20
85	Working with SAS and Microsoft 365	Chris Hemedinger	Presentation	proc step, proc sql, rest api, sas viya, proc export, proc import, excel	source	22
86	X-Ray Image Classification with Neural Networks	Riley Rutan, Eric C Braga, Xinyu Du	Machine learning has the potential to revolutionize the way that medical professionals review medical results to make diagnoses. A model that can classify large batches of medical imaging results to identify medical conditions and diseases can greatly improve patient outcomes as well as the efficiency and accuracy of radiologists and healthcare providers. Here, we created a neural network model that utilized deep learning techniques to analyze and classify medical images to identify a variety of diseases. A tool that can leverage machine learning to help medical professionals identify diseases more quickly and reliably will be an invaluable resource. We have focused our model on categorizing chest X-ray images to identify respiratory diseases such as COVID-19, viral and bacterial pneumonia, and tuberculosis. We have utilized neural networks that can identify and map the complex and subversive patterns that exist in the data stored in medical images.	neural network, feature extraction, data cleaning, machine learning, deep learning, network optimization, training set	source	10

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