MWSUG 2018 Papers - Index

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

#	Title ×	Authors ×	Abstract ×	Keywords ×	Pages
1	A Hands-on Introduction to SAS Metadata DICTIONARY Tables and SASHELP Views	Kirk Paul Lafler	SAS users can easily and quickly access metadata content with a number of read-only SAS data sets called DICTIONARY tables or their counterparts, SASHELP views. During a SAS session, information (known as metadata) is captured including SAS system options along with their default values, assigned librefs, table names, column names and attributes, formats, indexes, and more. This hands-on workshop introduces how metadata can be used as input into a SAS code generator or a SAS macro to produce the desired results, the application of specific DICTIONARY table and SASHELP view content, and an assortment of examples related to the creation of dynamic code	dictionary tables, proc step, proc sql, proc contents, excel, sas library, dataset, macro variable, sas log, select, options statement, proc print, proc freq	16
2	A Macro that Can Get the Geo Coding Information from the Google Map API	Ting Sa	This paper introduces a macro that can automatically get the geo coding information from the Google Maps API for the user. The macro can get the longitude, latitude, standard address, and address components like street number, street name, county or city name, state name, ZIP codes, and so on for the user. To use the macro, the user needs to provide only simple SAS input data. The macro then automatically gets the data and saves it to a SAS data set for the user. This paper includes all the SAS codes for the macro and provides the input data example to show you how to use the macro	dataset, create table, sample	6
3	A Macro to Calculate Sample Size for Studies Using the Proportional Time Assumption	Brian Mosier, John Keighley, Milind Phadnis	Sample size calculations for time-to-event outcomes are done mostly based on the assumption of proportional hazards or of exponentially distributed survival times. These assumptions are not appropriate for all scenarios and should not be implemented if the assumptions are not met. Phadnis et al1 introduces an alternative method using the assumption of proportional time by using the generalized gamma ratio distribution to calculate sample size. We developed a macro to calculate sample size needed for studies using the proportional time assumption for a given value of power in an efficient way. The macro automates the method from the paper to simulate survival data for two treatment arms with the test statistic following a generalized gamma ratio distribution. It then utilizes the bisection method in order to find the appropriate sample size needed for power, input by the user along with additional parameters	sample size, sample, proc power and sample size, effect size, survival function	7
4	A Macro to Import Subject Data Saved in a Location with Separate Subfolders for each Subject	Laurie Smith	Often, in Health Sciences, subject data is saved as one file per subject. When subject data is saved in separate files, it is often difficult to import each separate file into SAS without great effort. If data is organized with the subject ID as the folder name and each subject's data in the corresponding folder, this macro will allow a programmer to read the subject ID's from the folder names, and loop though each subjects folder, importing all data within each folder	do loop, filename statement, dataset	5
5	A Quick Look at Fuzzy Matching Programming Techniques Using SAS Software	Kirk Paul Lafler, Stephen B. Sloan	Data comes in all forms, shapes, sizes and complexities. Stored in files and data sets, SAS users across industries know all too well that data can be, and often is, problematic and plagued with a variety of issues. When unique and reliable identifiers are available, users routinely are able to match records from two or more data sets using merge, join, and/or hash programming techniques without problem. But, what happens when a unique identifier, referred to as the key, is not reliable or does not exist	proc step, proc sql, character variable, proc sort, sas function, missing value, data cleaning, numeric variable, proc format, character functions, order by, data transformation, proc contents, proc print, proc transpose	28
6	Addressing Opioid Crisis using Data Science	Rishabh Mishra	Opioids are commonly prescribed medication by doctors mainly used for treating acute and chronic pain. They are highly addictive and patients tend to become tolerant to the drug after a certain point in time. It means that patients either have to increase the dosage of the drug or stop taking it a nd both have their own set of disadvantages. On one hand, an overdose from these drugs can be fatal and on the other hand stopping these drugs can cause severe withdrawal symptoms and recurrence of pain. In this paper, opioid data has been used to predict the probability of a provider prescribing the opioid drugs. Prescription data will be used for this analysis. If the significant prescribers of opioids can be predicted, then it is possible that governmental action can be taken to avoid deaths. Opioids crisis is a serious ongoing problem and a lot of people are dying due to the overdose of these drugs	proc hpsplit, logistic regression	5
7	Advance Your Career with PROC TM!	John M. LaBore, Joshua M. Horstman	Most of us would like to advance our careers in one way or another. You may wish to become a respected technical expert in your field, move up into a leadership role, or even go independent as a consultant or small business owner. Regardless of your career aspirations, leadership and communication skills are critical to your success. Toastmasters International provides an avenue to develop these skills in a constructive and supportive environment. This paper will provide a summary of the Toastmasters program based on the authors' combined 20 years of experience with the program. We'll discuss how you can execute "`PROC` TM" and the benefits it can bring to your career	proc step, sas programmer	6
8	Alternative Methods of Regression when OLS is Not Right	Peter L. Flom	Ordinary least square regression is one of the most widely used statistical methods. However, it is a parametric model and relies on assumptions that are often not met. Alternative methods of regression for continuous dependent variables relax these assumptions in various ways. This paper will explore PROCS such as QUANTREG, ADAPTIVEREG and TRANSREG for these data. Keywords: Regression	regression analysis, proc step, proc quantreg, predictor variable, response variable	22
9	An introduction to classification and regression trees with PROC HPSPLIT.	Peter L. Flom	Classification and regression trees are extremely intuitive to read and can offer insights into the relationships among the IVs and the DV that are hard to capture in other methods. I will introduce these methods and illustrate their use with `PROC HPSPLIT`. Keywords: Trees	proc hpsplit, proc step	13
10	An Introduction to the Process of Improving a Neural Network	YuTing Tian	The top-level goal of this paper is to lay out a process for building a neural network in SAS . It is hoped that a reader can use the process, shown in this paper, as a template for building a Deep Neural Network. A lower level goal is to build a network that can outperform a network in a paper by one of my professors. Deep learning is a kind of neural network and a specific kind of machine learning (e.g. artificial intelligence). Deep learning is a recent and powerful machine learning algorithm that enables a computer to build a multi-layer non-linear model. Even though deep neural networks are popular, not many papers discuss the overall process of building a neural network in SAS. This paper explores a practical application, associated with the process of deep neural network in SAS Enterprise Miner	data mining, neural network, proc hpsplit, missing value, feature selection, deep learning, confusion matrix, proc varclus, proc step, outlier, data cleaning	22
11	An Update on the CS Standard Analyses and Code Sharing Working Group	Nancy Brucken, Jared Slain	The Standard Analyses and Code Sharing Computational Science (CS) Working Group is providing recommendations for analyses, tables, figures, and listings for data that are common across therapeutic areas (laboratory measurements, vital signs, electrocardiograms, adverse events, demographics, medications, disposition, hepatotoxicity, pharmacokinetics). The working group has published seven white papers, and has also created an online platform for sharing code. Crowd-sourcing code development will enable consistent interpretation of methods and substantial savings in resourcing across the industry. This presentation will provide an update on these efforts	adverse events, clinical trial, vital signs	4
12	Analyzing Amazon's Customer Reviews using SAS Text Miner for Devising Successful Product Launch Strategies	Manideep Mellachervu, Anvesh Reddy Minukuri	Digital economy is showing a tremendous growth in the 21st century and it is having a massive impact in the current society. E-commerce is one element of the Internet of Things and its worldwide retail sales amounted to 2.3 trillion US dollars.1 This shows the popularity of online shopping and it indicates an evolution of retailers in this industry. A recent study conducted by GE Capital Retail Bank has found that 81% of consumers perform online research before buying products.2 This tells that consumers rely heavily on others' opinions and experiences in order to buy a product. Businesses need to understand customers' view of their products and competitors' products for strategic marketing. E-commerce businesses provide a platform to generate user-experience content through customer reviews, which are vital for a buyer to choose the best product out of numerous similar products available in the market	cluster analysis, proc freq, parsing, data mining	13
13	Analyzing YouTube comments on gun violence using SAS Viya and SAS Text Miner	Sridevi Loya	Gun violence is a major cause of premature death in the U.S. "Guns kill more than 38,000 people and cause nearly 85,000 injuries each year" according to the American Public Health Association [1]. This paper focuses on studying the public opinions and reforms of gun violence in schools within the United States of America. This research include s studying and understanding the views of people after the occurrence of the P arkland, Florida mass shooting. Text analytics is performed on the comments and replies written by people on YouTube. This paper is specially looking at two specific sites for textual data, the CNN news and the ABC news site. These comments were written by people after the incident occurred. This paper will be of benefit as the main aim of this paper is to come up with potential solutions by looking in detail for people's opinion, derive meaningful insights from the comments and analyze if people have any viable recommendations to mitigate such acts	proc freq, sas base, data mining, parsing, proc import, proc step, excel	9
14	Animate Your Data!	Richann J. Watson, Kriss Harris	When reporting your safety data, do you ever feel sorry for the person who has to read all the laboratory listings and summaries? Or have you ever wondered if there is a better way to visualize safety data? Let's use animation to help the reviewer and to reveal patterns in your safety data, or in any data! This hands-on workshop demonstrates how you can use animation in SAS 9.4 to report your safety data, using techniques such as visualizing a patient's laboratory results, vital sign results, and electrocardiogram results and seeing how those safety results change over time. In addition, you will learn how to animate adverse events over time, and how to show the relationships between adverse events and laboratory results using animation. You will also learn how to use the `EXPAND` procedure to ensure that your animations are smooth. Animating your data will bring your data to life and help improve lives!	series plot, change from baseline, box plot, proc step, adverse events, by group processing, ods graphics, output delivery system, sample	18
15	Application of heavy-tailed distributions using PROC IML, NLMIXED and SEVERITY	Palash Sharma	Probabilistic heavy-tailed distribution (Pareto, Weibull, Burr etc.) theory has vast applications involving in many real-life situations and natural phenomena. This area of research attracts not only for theoretical probabilistic nature but also for various branches of statistics. Heavy-tailed distribution are also used for modeling various biological, actuarial, financial, economic, hydrological, and engineering data. In this paper, we are modeling the cancer patient dataset and electrical blackouts dataset in the USA with generalized Pareto distribution. We also simulate the data arising from generalized Pareto distribution and estimate the parameters of generalized Pareto distribution (GPD) using maximum likelihood estimation. A suite of SAS procedure is used for all computation, specifically `PROCEDURE IML`, SEVERITY,	proc step, likelihood function, akaike information criterion, maximum likelihood estimation, proc iml, proc calis, sample	14
16	Assessing Model Adequacy in Proportional Hazards Regression	Michael G. Wilson	Proportional Hazards regression has become an exceedingly popular procedure for conducting analysis on right-censored, time-to-event data. A powerful, numerically stable and easily generalizable model can result from careful development of the candidate model, assessment of model adequacy, and final validation. Model adequacy focuses on overall fitness, validity of th e linearity assumption, inclusion (or exclusion) of a correct (or an incorrect) covariate, and identification of outlier and highly- influential observations. Due to the presence of censored data and the use of the partial maximum likelihood function, diagnostics to assess these elements in proportional hazards regression compared to most modeling exercises can be slightly more complicated. In this paper, graphical and analytical methods using a rich supply of distinctive residuals to address these model ade quacy challenges are compared	regression analysis, hazard function, likelihood function, proc phreg, cumulative hazard, proportional hazards assumption, insurance risk score, proc calis, survival analysis, influential observation, estimate, correlation, sample, data cleaning, covariance, maximum likelihood estimation, exponential distribution, frailty model, bootstrap, type ii error, adverse events, predictor variable	37
17	Automated Transfer of a Sea of SAS Programs between Data Transfers	Xuelin Li, Jiangang Cai, Cindy Lee	In pharmaceutical industries, huge number of SAS programs are rerun routinely to refresh SDTM/ADaM data sets and TFLs (Tables, Figures, Listings) in different folder locations to accommodate data transfers. Manually updating the paths within the programs can be tedious. In this paper, we developed a method to move the SAS programs to the new location with automated path update within the programs. This method facilitates the process of moving SAS programs from one location to another location seamlessly and save hours of programmers' time in updating every program. More importantly, this automation process eliminates the chance for human errors	macro variable, dataset, infile statement, temporary dataset, cdisc	6
18	Automatic Indicators for Dummies: A macro for generating dummy indicators from category type variables	Matthew Bates	Dummy Indicators are critical to building many statistical models based on data with category type predictors. Most programmers rely on the "class" option within various procedures to temporarily build such predictors behind the scenes. This method carries with it a variety of limitations that can be overcome by auto-generating dummy indicators of all variables below a reasonable threshold of cardinality prior to running such procedures. Statistical modelers may find this topic a real effort and time saver while advanced SAS programmers looking for creative techniques of efficiently automating processes may find this macro worth geeking out over	binary variable, macro variable, hash table, do loop, variable list, lookup	11
19	AutoPDF : an R Package to Output Vector Graphics	Hillary Graham, Zeqing Lu, Michelle Carlsen	Typically pharmaceutical statistical analysts output graphics in RTF format. However, RTF files are difficult to alter for publication or submission because they are not an editable file type. On the other hand, vector-based files enable users to modify colors, sizes, labels, etc. in Adobe Illustrator before disclosure. Currently, R has the capability to create vector graphics in PDF format. However, the process of outputting graphics in PDF format from R requires several steps. This can be irritating for experienced users, and discouraging for new users. In order to make the process easier, we have generated R code to output graphics into a PDF. This code automates the creation of vector-based graphics in R, making it more efficient for both new and experienced R-users to prepare plots ready for disclosure	if-then-else, ods pdf	11
20	Avoiding Sinkholes: Common Mistakes During ADaM Data Set Implementation	Richann J. Watson	The ADaM Implementation Guide was created in order to help maintain a consistency for the development of analysis data sets in the pharmaceutical industry. However, since its inception we have seen issues with guideline non- conformance which can impede this development process and carry impacts that are felt down-stream in subsequent processes. When working with ADaM data sets, non-compliance and other related issues are likely the number one source for numerous hours of re-work; not only creating unnecessary additional work for the data sets themselves, but also for reports, compliance checks, the Analysis Data Reviewers Guide (ADRG), etc. all the way down to the ISS/ISE processes. Considering this breadth of impact, one can see how devastating these sinkholes can be. Like any sinkhole, there is a way out of it but it is a long, tedious process that will consume a lot of resources and it is always better to avoid the sinkhole entirely	sdtm domain, imputation, raw data, change from baseline, crossover study, per protocol, interim analysis, treatment period, adverse events, cdisc	15
21	Backsplash Patterns for Your World: A Look at SAS OpenStreetMap (OSM) Tile Servers	Barbara B. Okerson	Originally limited to SAS Visual Analytics, SAS now provides the ability to create background maps with street and other detail information in `SAS/GRAPH` using open source map data from OpenStreetMap (OSM). OSM provides this information using background tile sets available from various tile servers, many available at no cost. This paper provides a step-by-step guide for using the SAS OSM Annotate Generator (the SAS tool that allows use of OSM data in SAS). Examples include the default OpenStreetMap tile server for streets and landmarks, as well as how to use other free tile sets that provide backgrounds ranging from terrain mapping to bicycle path mapping. Dare County, North Carolina is used as the base geographic area for this presentation	macro variable, proc step, proc gmap	13
22	Baseline Mean Centering for Analysis of Covariance (ANCOVA) Method of Randomized Controlled Trial Data Analysis	Jennifer Scodes	Many analytical approaches exist to compute treatment effects and within-group changes from baseline for data analysis of randomized controlled trials with multiple follow-up visits. One of these approaches is the analysis of covariance (ANCOVA) method, in which baseline values are included as a covariate instead of as an outcome. Using the ANCOVA method, the treatment effects can be easily computed from model estimates; however, within-group changes from baseline cannot be directly computed in SAS procedures without centering the outcome measures by the overall baseline mean. This paper will present a macro that can be used to analyze data from two-arm randomized controlled trials using the ANCOVA method to compute and present both treatment effects and within-group changes from baseline using baseline mean centering. This paper is intended for all levels of SAS users that analyze clinical trial data	treatment effect, change from baseline, ancova, response variable, covariance, proc step	6
23	Bootstrap Linear Mixed-Effects Models using SAS Procedures	Adams Kusi Appiah	The bootstrap resampling technique is a general method for estimating the sampling distribution of a statistic of interest and is applied in many research applications. It can obtain more robust parameter estimates and confidence intervals in situations where no assumptions about the underlying distribution of the model are available. However, the main concern of the bootstrap method is how to generate a bootstrap distribution to resemble the true distribution of the observed data. In the context of linear mixed effects models, the distribution of samples should be generated to account for between-subject variability and residual variability in the data. This paper illustrates various ways to bootstrap data for linear mixed- effects models using the SURVEYSELECT and MIXED procedures available with `SAS/STAT` software. These methods were applied to assess the uncertainty of parameter estimates in linear mixed effect models with data from the National Cooperative Gallstone Study (NCGS)	bootstrap, sample, proc mixed, proc step, estimate, restricted maximum likelihood, confidence interval, fixed effects, maximum likelihood estimation, proc surveyselect, normal distribution, proc calis, likelihood function, random sampling, covariance, proc univariate, longitudinal data	17
24	Computer Karma: Non-Monetary Benefits from Statistical And Information Technology Volunteer Work	Brandy R. Sinco	Volunteer statistical and information technology work offers many rewards other than direct monetary payment. Many of these rewards are at least as valuable as money. First, the volunteer can enhance one's skills, which will be useful in a present or future job. Examples with SAS range from simple techniques, such as computing intraclass correlation coefficients with `PROC MIXED` to complex analysis methods, such as learning how to bootstrap indirect effects with `PROC CALIS`. Other non-SAS examples are learning how to debug error messages about audio links in web pages and expanding knowledge of computer trouble shooting. In order to be successful as a data scientist, analysts must be continually willing to expand their skills through continuing education. Volunteer work enhances and supplements formal classes and workshops, by providing learning opportunities without the rigid deadlines in a paid job. Second, the volunteer can network with people who may later assist in finding a job	proc step, error message, confidence interval, proc mixed, correlation, likelihood function, data science	11
25	Confounded? This example shows how to use SAS chi-square tests, correlations and logistic regression to unconfound a result.	Michael C. Grierson	The purpose of this paper is to describe an example of how to unconfound a confounded statistical result 1 and to present a recipe for unconfounding a result. The confounded result is the conclusion that since African American student loan borrowers are more likely to default on their student loans, that "These results show that the U.S. Department of Education cannot ignore the interaction of race and student loans". This paper shows that student loan defaults are more (by about 5 times) associated with lower median income status than race	default, default rate, correlation, proc step, sample, logistic distribution, chi-square test, logistic regression, proc univariate, proc logistic, regression analysis, proc corr, spearman correlation, proc rank, proc freq	13
26	Conversion of CDISC specifications to CDISC data - specifications driven SAS programming for CDISC data mapping	Yurong Dai, Jiangang Jameson Cai	This is for a metadata driven approach that utilize SAS programming techniques for SDTM and ADaM data mapping. Metadata extracted from specifications are converted into data set's attributes, format, variable names and their order and sorting order for specification implementation in our reference code. It increases code's reusability, efficiency and consistency between data specifications and output data, and reduced re-work after data specification's update, during code development for SDTM mapping and ADaM data sets derivation	macro variable, proc format, proc step, proc sql, cdisc	9
27	Creating suicide attempt/intentional self-harm episodes using administrative billing data	Jamie Kammer, Mahfuza Rahman, Qingxian Chen	In identifying the services that individuals received in the time prior to and following a suicide attempt or intentional self-harm episode, it is important to separate out those services that occurred as part of treatment during the emergency room or inpatient visit (including transfers) from services outside of the episode. Specifically, it is useful to understand the service utilization patterns individuals experienced directly prior to a suicide attempt, whether individuals were engaged in outpatient care immediately following emergency room or inpatient treatment, and whether individuals experienced separate suicide attempts outside of an index episode. These insights can inform public health efforts to prevent and treat suicide attempts and intentional self-harm. There are several considerations to take into account when using administrative billing data for public health research and there is a need for standardizing methods around identifying episodes carefully	binary variable	7
28	Customized Project Tracking with SAS and Jira	Nancy Brucken	As programmers and statisticians, most of us are far better at programming tasks than we are at project management. Jira is a powerful and inexpensive commercially-available application designed to help programming teams track their progress on projects. It is built on top of a PostgreSQL database, which can be queried from SAS to generate a variety of custom reports	data cleaning, sample	8
29	Data For Good as a Community Service Project at Work	David J. Corliss	Many businesses large and small support volunteering within the community, often sponsoring volunteer work days where employees can attend a well-organized activity for a group doing good work in the community. Today, Data For Good volunteering an opportunity to supply a critical skill for charitable organizations making a difference. This presentation is designed to help business leaders set up a Data For Good project as community service and team building activity, with best practices for finding a good organization to support, recruiting participants, managing the volunteer work day, and sharing the story with the wider community. Descriptions of successful projects and how to do them include building a membership database, data dive events to develop recruiting models for community service groups, seasonal optimization of resources (e.g., food pantries), and others. Practical, proven processes are presented to make Data For Good your company's next community service project	data science, root cause analysis	6
30	Data-Driven Data Analysis	Jack N. Shoemaker	When confronted with a new data channel, the modern data scientist or analyst will employ sophisticated data visualization tools like Visual Analytics to size up the data. Not all users have access to these tools and must rely on more pedestrian code-based approaches. This paper explores techniques using Base SAS to provide data-driven data analysis to help size up data absent the more modern tools. These techniques leverage the details about data available from the dictionary subsystem. Knowing the names, formats, and data types of the data allows one to derive great insight into the content of the data stream	proc step, proc sql, proc freq, macro definition, dataset, user-defined format, data step, proc report, sample, data science, macro variable, missing value, proc univariate, create table, macro loop, label statement	10
31	Dating for SAS Programmers	Joshua M. Horstman	Every SAS programmer needs to know how to get a date... no, not that kind of date. This paper will cover the fundamentals of working with SAS date values, time values, and date/time values. Topics will include constructing date and time values from their individual pieces, extracting their constituent elements, and converting between various types of dates. We'll also explore the extensive library of built-in SAS functions, formats, and informats for working with dates and times using in-depth examples. Finally, you'll learn how to answer that age-old question... when is Easter next year?	date variable, sysdate9, datetime, time, macro variable, date and time functions, datepart, automatic macro variable, user-defined format, dhms, sas programmer, options statement, macro function	13
32	Differentiate Yourself	Kirk Paul Lafler	Today's employment and business marketplace is highly competitive. As a result, it is necessary for SAS professionals to differentiate themselves from the competition. Success depends on a number of factors, including positioning yourself with the necessary technical skills in relation to the competition. This presentation illustrates how SAS professionals can acquire a wealth of knowledge and enhance their skills by accessing valuable and free web content related to SAS. With the aid of a web browser and an Internet connection, anyone can access published PDF papers, Microsoft Word documents, Microsoft PowerPoint presentations, student notes, instructor lesson plans, hands-on exercises, webinars, audios, videos, a comprehensive technical support website maintained by SAS, and more to acquire the essential expertise that is needed to cut through all the marketplace noise and begin differentiating yourself to secure desirable opportunities with employers and clients	sas programmer, sas consultant	20
33	Efficiency Programming with Macro Variable Arrays	Veronica Renauldo	Macros in themselves boost productivity and cut down on user errors. However, most macros are not robust and serve only a few specific repetitive purposes. Just like arrays increase the efficiency of a `DATA STEP`, macro variable arrays increase the efficiency of a macro. Macro variable arrays allow the macro to function more autonomously than what is typical for macro processing and work in all SAS platforms that support macro processing. Automating the process of determining the number of times a macro needs to be utilized for a task is just one of the several applications of macro variable arrays. There are numerous ways to create macro variable arrays such as `%LET` statements, `PROC SQL`, and CALL SYMPUT statements; each with their own user-friendly approach. Macro variable arrays employ the use of loops and logic to construct comprehensive macros allowing for a multitude of output types functioning within one macro call	macro variable, character functions, call symput, proc sql, proc step, macro function, &variable, correlation, sashelp, macro put, by group processing, macro call, macro definition, proc freq	19
34	Factors Responsible for Students' Enrollment at Oklahoma State University	Parag Vilas Sasturkar	It is crucial for a university to have bright students in order to become educational leaders in this increasingly competitive world. The Institutional Research and Information Management (IRIM) department at Oklahoma State University (OSU) has been playing a vital role in campus decision -making, managing institutional performance, and providing information, research, and analysis on demand. Therefore, i t is very important for IRIM to collect and provide accurate information to market OSU i.e. target to the right audience (prospective students) every year. The main purpose of this resear ch project is to help IRIM to evaluate different factors that have been driving an undergraduate student to enroll at OSU. This research will attempt to determine students who have a better chance or probability of choosi ng OSU over other universities. The dataset consists of OSU students' data including demographi cs, admission data , and part-time employment and healthcare activities	proc means, data science	6
35	Finding National Best Bid and Best Offer - Quote by Quote	Mark Keintz	U.S. stock exchanges (currently there are 12) are tracked in real time via the Consolidated Trade System (CTS) and the Consolidated Quote System (CQS). CQS contains every updated quote from each of these exchanges, covering some 8,500 stock tickers. It provides the basis by which brokers can honor their fiduciary obligation to investors to execute transactions at the best price, i.e. at the NBBO (National Best Bid or Best Offer). With the advent of electronic exchanges and high frequency trading (timestamps are published to the microsecond), data set size (approaching 1 billion quotes requiring 80 gigabytes of storage for a normal trading day) has become a major operational consideration for market behavior researchers recreating NBBO values. This presentation demonstrates a straightforward use of hash tables for tracking constantly changing quotes for each ticker/exchange combination to provide the NBBO for each ticker at each time point in the trading day	hash table, where statement	6
36	From Clicking to Coding: Using ODS Graphics Designer as a Tool to Learn Graph Template Language	Margaret Kline, Daniel Muzyka	`ODS` Graphics Designer brings simple graphics creation to SAS platforms 9.2 and later. This application enables any novice user who can navigate an interactive point-and-click menu to generate highly customizable graphical representations. `ODS` Graphic Designer which functions in conjunction with the suite of SAS products can be invoked to facilitate the creation of Graph Template Language (GTL) through a non-intimidating interface. Not only can this code be edited at a subsequent time but providing novice users with the instant gratification of a striking graphic display could encourage the continued expansion of SAS skills or ease the transition from other software. There is untapped potential in `ODS` Graphics Designer as an educational tool which exists in its ability to acquaint users with the underlying syntax of GTL	box plot, proc step, proc sgplot, graph template language	9
37	From Readability to Responsible Risk Management: Facilitating the Automatic Identification and Aggregation of Software Technical Debt within an Organization Through Standardized Commenting in SAS Program Files and SAS Enterprise Guide Project Files	Troy Martin Hughes	Software readability is greatly improved when programs include descriptive comments in a predictable, standardized format. Program headers that describe software requirements, author, creation date, versioning history, caveats, and other metadata are a common method to facilitate a greater understanding of software objectives, strengths, weaknesses, and prerequisites. Moreover, when program headers are standardized, they are not only more readable to developers but also to parsing algorithms that can automatically extract metadata for analysis or archival. Comments throughout software can also improve its readability and, when constructed in a standardized format , can be parsed automatically and saved in control tables . This text introduces a standardized commenting methodology that enables both qualitative and quantitative comments to be parsed from SAS software headers and body	sas enterprise guide, sas enterprise guide project, macro variable, sample, label statement, data validation rules, quality control, variable, call symput, macro definition, likelihood function, comment, filename statement, data cleaning	35
38	From Stocks to Flows: Using SAS HASH objects for FIFO, LIFO, and other FO's	Mark Keintz	Tracking gains or losses from the purchase and sale of diverse equity holdings dep ends in part on whether stocks sold are assumed to be from earliest lots acquired (a FIFO queue) or the latest lots acquired (LIFO). Other inventory tracking applications have a similar need for application of either FIFO or LIFO rules. This presentation shows how a collection of simple ordered hash objects, in combination with a hash -of- hashes is a made-to-order technique for easy data-step implementation of FIFO, LIFO, and other less- likely rules, like HIFO (highest price first out) and LOFO (lowest price)	hash table, hash iterator	13
39	Genocide Modeling - Historical Risk Factors and Odds Ratios	David J. Corliss	This analysis identifies risk factors associated with genocide events. A review of historical conflicts where genocide was present in some and not others provided the data. Using these data, Decision Tree and Random Forest models identify variables with measurable association with genocide events. Logistic Regression and Decision Tree methods are applied to the screened list of variables. Odds ratios are calculated to assess the relative risk of different factors. These models are used to assess the relativ e likelihood of genocide occurring or developing in the near year in various countries	proc hpsplit, feature selection, proc step	5
40	Getting Started with the SGPLOT Procedure	Joshua M. Horstman	Do you want to create highly-customizable, publication-ready graphics in just minutes using SAS? This workshop will introduce the `SGPLOT` procedure, which is part of the `ODS` Statistical Graphics package included in Base SAS. Starting with the basic building blocks, you'll be constructing basic plots and charts in no time. We'll work through several different plot types and learn some simple ways to customize each one	proc sgplot, bar chart, vertical bar chart, scatter plot, nominal variable, line chart, box plot, response variable, histogram, proc step, bubble plot, heatmap, series plot, sas base, density plot, group by, regression analysis	18
41	Great Time to Learn GTL	Richann J. Watson, Kriss Harris	It's a Great Time to Learn GTL! Do you want to be more confident when producing GTL graphs? Do you want to know how to layer your graphs using the OVERLAY layout and build upon your graphs using multiple LAYOUT statement? This paper guides you through the GTL fundamentals!	bar chart, macro variable, proc template, proc step	12
42	Handling Missing Data in Exploratory Factor Analysis Using SAS	Min Chen	Exploratory factor analysis (EFA) is a statistical technique to reduce the dimension of multivariate data and to explore the latent structure within the data. Missing data is almost inevitable while conducting EFA. By default, the `FACTOR` procedure will only include the complete cases which most of the time it is not the first choice of researchers. Given EFA could be performed on individual-level data, correlation or covariance matrix, different formats of data could be fed into SAS and different missing data handling techniques could be applied. This article will demonstrate the above with SAS examples, and comment briefly on how this is generally handled in other statistical software	missing value, covariance, proc factor, factor analysis, correlation, maximum likelihood estimation, likelihood function	3
43	How to Avoid Possible Tricks When Using DATA STEP MERGE Instead of PROC SQL JOIN	Guangtao Gao		proc sql, proc step, format statement, length statement, data merging, sas programmer	27
44	How to HOW: Hands-on-Workshops Made Easy	Chuck Kincaid	Have you ever attended a Hands-on-Workshop and found it useful? Many people do! Being able to actually try out the things that you're learning is a wonderful way to learn. It's also a great way to teach. You can actually see the people apply what they're learning. Have you ever thought that it would be fun to teach other people in a hands on format? Maybe you weren't sure what it takes or how to approach the course. This presentation will help you with those questions and struggles. What to teach? How much to teach? How should I teach it? How is a Hands-on- Workshop different than lecture style? How much to put into PowerPoints? What if they ask me something I don't know? What if they have a computer problem? All those questions that you have will be answered in this presentation	proc step, proc sgplot	8
45	How to Navigate in a Maze of the Raking Macro with Advanced Weight Trimming	Michael P. Battaglia, David Izrael, Sarah Ball	Raking to population control totals is often the final step in developing survey weights. Raking is an iterative procedure that brings the weighted sample into agreement on socio-demographic variables that are available for the sample and the population. It is primarily used to reduce unit nonresponse bias. Raking can lead to some observations ending up with extreme weights; in other words, weights that are very large or very small compared to the mean weight, resulting in inflated standard errors. In 2009, we enriched a SAS raking macro implementing weight trimming during the raking iterations, ensuring that the weighted sample agreed with the population. We recently further enhanced the macro to add several options related to weight trimming. Among them, two trimming methods - "AND" or '"OR" - and an option that allows us to set some different convergence criteria for a subset of the raking variables	control total, sample	13
46	How to Score Big with SAS Solutions: Various Ways to Score New Data with Trained Models	Scott Koval	After training a statistical model, the next step is to put it into production in order to score new data. While it might be tempting to manually write code to score data, this can lead to problems with precision, complexity, and updating. SAS solutions offer a wide variety of methods to do this. This paper covers several common techniques, including `PROC SCORE`, the CODE and STORE statements, and `PROC ASTORE`. By learning these approaches, SAS can easily put complex models to work	proc step, champion model, estimate, regression analysis, proc score, score code, data mining	6
47	How to Succeed in Consulting	Chuck Kincaid	Maybe you are an awesome programmer working on a company's internal consulting team, but you have a hard time getting work done by the deadline. Maybe you are a strong SAS developer who does independent consulting, but clients get upset when changes they ask for cost them more money. Just because you're good in the technical skills doesn't mean that you can succeed as a consultant. This presentation will give you tips on how to do just that. With a combination of project management and consulting skills you can go much farther, whether it's doing internal consulting, independent consulting or working for a consulting company. This presentation will be good for people who want to do better at managing the world outside of their code	data science, statistical programmer	15
48	Improving Plots Using XAXISTABLE and YAXISTABLE	Jacob Keeley, Carl Nord	New to the `SGPLOT` procedure for SAS 9.4TM, the XAXISTABLE and YAXISTABLE statements respectively create an X/Y axis aligned row/column of textual data placed at specific locations in relation to the primary plot within the given `SGPLOT` procedure. The XAXISTABLE and YAXISTABLE statements are applicable with any primary plot, aside from BAND, BLOCK, FRINGE, REG, LOESS, and PBSPLINE plots. Along with directing the X, Y coordinates of the supplementary data values, there are many options accompanying the XAXISTABLE/YAXISTABLE statements which allow the user to change the color, order, and position of the accompanying row(s) of data. The XAXISTABLE statement proves its worth when used in conjunction with survival analysis. When dealing with Kaplan Meier survival curves, t he XAXISTABLE statement essentially allows the user to personalize their own at risk tables when the `LIFETEST` procedure lacks the functionality necessary for the request	proc sgplot, proc step, kaplan-meier plot	8
49	Keeping Up to Date with ODS Graphics	Warren F. Kuhfeld	SAS 9.4M5 provides you with several enhancements to `ODS` Graphics, including the new `SGMAP` procedure. Y ou can use `PROC SGMAP` to create maps and superimpose graphs such as bubble plots. Bar charts in existing procedures such as `PROC SGPLOT` have new options for fill patterns and fill types. New options for box plots enable you to display statistics and control the caps on whiskers. Other options enable you to modify tick labels, tick styles, legends, baselines, and reference line thickness. Y ou can also control the image names when there are BY groups. This paper describes and illustrates these recent updates	proc step, proc sgplot	18
50	Leveraging SHEWHART Procedure Options to Monitor and Evaluate Improvements in Healthcare	Roderick Jones, Lynn (Xiaohong) Liu	In healthcare, the purpose of statistical process control (SPC) is often to quantify improvements and identify unintended consequences resulting from an intentional change in an environment, policy, treatment protocol, or decision-support tool. `SAS/QC` facilitates the production of statistics and their visualization through the `SHEWHART` procedure. Unlike in manufacturing, process change - rather than stability - is commonly sought, and interventions might be frequent and staggered over time	proc step, macro variable, out=	12
51	Logistic and Linear Regression Assumptions: Violation Recognition and Control	Deanna Naomi Schreiber-Gregory, Karlen Bader	Regression analyses are one of the first steps (aside from data cleaning, preparation, and descriptive analyses) in any analytic plan, regardless of plan complexity. Therefore, it is worth acknowledging that the choice and implementation of the wrong type of regression model, or the violation of its assumptions, can have detrimental effects to the results and future directions of any analysis. Considering this, it is important to understand the assumptions of these models and be aware of the processes that can be utilized to test whether these assumptions are being violated. Given that logistic and linear regression techniques are two of the most popular types of regression models utilized today, these are the ones that will be covered in this paper. Some Logistic regression assumptions that will reviewed include: dependent variable structure, observation independence, absence of multicollinearity, linearity of independent variables and log odds, and large sample size	regression analysis, logistic regression, proc reg, predictor variable, logistic distribution, correlation, sample, outlier, response variable, sample size, cook's distance, scatter plot, covariance, multicollinearity, standard deviation, proc calis, box plot, condition index, normality test, proc step, proc glm	22
52	Making Your SAS Output, Results, Reports, Charts and Spreadsheets More Meaningful with Color	Kirk Paul Lafler	Color can help make SAS results, reports, tables, charts and spreadsheets more professional and meaningful. Instead of producing boring and ineffective results, users can enhance the appearance of their output to highlight and draw attention to important data elements and issues, including headings, subheadings, footers, minimum and maximum values, ranges, outliers, special conditions, and other elements. Color can be added to text, foreground, background, row, column, cell, summary, and total with amazing color and traffic light ing scenarios. Topics include exploring an assortment of examples to illustrate the various ways results, documents, reports, tables, charts and spreadsheets can be enhanced with color, effectively add color to PDF, RTF, HTML, and `EXCEL` spreadsheet results using `PROC PRINT`, `PROC FREQ`, `PROC REPORT`, `PROC TABULATE`, and `PROC SGPLOT` and Output Delivery System (`ODS`) with style	bar chart, proc sgplot, proc step, histogram, proc sgpanel, excel, scatter plot, vertical bar chart, box plot, bubble plot, proc freq, series plot, proc report, data science, class variable, correlation, outlier	20
53	Merge with Caution: How to Avoid Common Problems when Combining SAS Datasets	Joshua M. Horstman	Although merging is one of the most frequently performed operations when manipulating SAS datasets, there are many problems which can occur, some of which can be rather subtle. This paper examines several common issues, provides examples to illustrate what can go wrong and why, and discusses best practices to avoid unintended consequences when merging	merge statement, by group processing, in=, out=, set statement, dataset, if-then-else, subsetting if, sas log, pdv, proc sql, length statement	16
54	Order, Order! Four Ways to Reorder Your Variables, Ranked by Elegance and Efficiency	Louise S. Hadden	SAS practitioners are frequently required to present variables in an output data file in a particular order, or standards may require variables in a production data file to be in a particular order. This paper and presentation offer several methods for reordering variables in a data file, encompassing both `DATA STEP` and procedural methods. Relative efficiency and elegance of the solutions will be discussed	dataset, proc sql, proc step, length statement, proc contents, array, attrib statement, sample	20
55	Pan-Cancer Epigenetic Biomarker Selection from Blood Sample Using SAS	Xi Chen, Jin Xie, Qingcong Yuan		sample, correlation, sample size, machine learning, pearson correlation, neural network, proc hpforest, response variable	9
56	Perl Regular Expression - The Power to Know the PERL in Your Data	Kaushal Chaudhary, Dhruba Ghimire	Perl regular expression is one of the powerful and efficient techniques for complex string data manipulation. SAS offers regular expression engine in the base SAS without any additional license requirement. This would be a great addition to a SAS programmers' toolbox. In this paper, we present basics of the Perl regular expression and various Perl regular functions and call routine such as PRXPARSE(), PRXMATCH(), and CALL PRXCHANGE () etc. with examples. The presentation is intended for beginner and intermediate SAS programmers	prxparse, prxchange, prxposn	10
57	Propensity Scores and Causal Inference for (and by) a Beginner	Bruce S. Lund	In an observational study the subjects are assigned to treatments through a non -randomized process. In the simplest and most typical case there are two treatments, one being deemed as "control". Associated with the subjects is an "outcome" which is of interest to the researcher. The outcome could be discrete, very often binary, or have continuous numeric values. The researcher wants to know the effect of the treatment on the outcome. This is called the average treatment effect and abbreviated by ATE. Due to the non-random assignment of treatments, a simple comparison of outcomes, such as an average per treatment group, would be biased. One solution to removing the bias rests on finding covariates for the subjects such that the treatment assignment can be regarded as random for subjects having essentially equal covariate values. This is called "covariate balance" for the treatments	propensity score, proc step, sample, standard deviation, proc ttest, potential outcomes, inverse probability weighting, treatment effect, confidence interval, average treatment effect, causal inference, response variable	18
58	Purrfectly Fabulous Feline Functions	Louise S. Hadden	Explore the fabulous feline functions and calls available in SAS 9.1 and later. Using CAT functions and CAT CALLs gives you an easier way to streamline your SAS code and facilitate concatenation of character strings. So, leave verbose coding, myriad functions, and the vertical bar concatenation operators behind! SAS 9.2 (and beyond) enhancements will also be demonstrated	character functions, proc contents, sas log, cpu time, proc step, length statement, catq, character variable	15
59	Quality Control for Big Data: How to Utilize High Performance Binning Techniques	Deanna Naomi Schreiber-Gregory, Karlen Bader	It is a well-known fact that the structure of real-world data is rarely complete and straightforward. Keeping this in mind, we must also note that the quality, assumptions, and base state of the data we are working with has a strong influence on the selection and structure of the statistical model chosen for analysis and/or data maintenance. If the structure and assumptions of the raw data are altered too much, then the integrity of the results as a whole are grossly compromised. The purpose of this paper is to provide programmers with a simple technique which will allow the aggregation of data without losing information. This technique will also check for the quality of binned categories in order to improve the performance of statistical modeling techniques	proc step, interval variable	12
60	Regularization Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets	Deanna Naomi Schreiber-Gregory, Karlen Bader	Multicollinearity can be briefly described as the phenomenon in which two or more identified predictor variables are linearly related, or codependent. The presence of this phenomenon can have a negative impact on an analysis as a whole and can severely limit the conclusions of a research study. In this paper, we will briefly review how to detect multicollinearity, and once it is detected, which regularization techniques would be the most appropriate to combat it. The nuances and assumpt ions of R1 (Lasso), R2 (Ridge Regression), and Elastic Nets will be covered in order to provide adequate background for appropriate analytic implementation. This paper is intended for any level of SAS user. This paper is also written to an audience with a background in theoretical and applied statistics, though the information within will be presented in such a way that any level of statistics/mathematical knowledge will be able to understand the content	regression analysis, ridge regression, elastic net, lasso regression, proc step, predictor variable, proc reg, response variable, feature selection, estimate, proc glmselect, correlation, bootstrap, proc calis, sample, quadratic programming, proc univariate, logistic distribution, condition index, proc corr	23
61	Screening, Transforming, and Fitting Predictors for Cumulative Logit Model	Bruce S. Lund	The cumulative logit model is a logistic regression model where the target (or dependent variable) has 2 or more ordered levels. If only 2 levels, then the cumulative logit is the binary logistic model. Predictors for the cumulative logit model might be "NOD" (nominal, ordinal, discrete) where typically the number of levels is under 20. Alternatively, predictors might be "continuous" where the predictor is numeric and has many levels. This paper discusses methods that screen and transform both NOD and continuous predictors before the stage of model fitting. Once a collection of predictors has been screened and transformed, the paper discusses predictor variable selection methods for model fitting. One focus of this paper is determining when a predictor should be allowed to have unequalslopes. If unequalslopes are allowed, then the predictor has J-1 distinct slopes corresponding to the J values of the target variable	logistic regression, logistic distribution, proc logistic, proc step, likelihood function, degrees of freedom, significance level, model training, regression analysis, macro call, predictor variable, response variable, feature selection, macro parameter, macro definition, hypothesis testing, ordinal logistic regression, proc genmod, correlation, sample, binary variable, proc freq	22
62	Seeing the Forest for the Trees: Part Deux of Defensive Coding by Example	Donna E. Levy, Nancy Brucken	As statisticians and programmers, SAS is part of our daily life. Through assessing patterns, data quality, programming datasets, analysis displays or developing simulations, we need to determine the best ways to conduct our daily work, allowing us to see the forest for the trees. This paper provides guidance on quality defensive programming, efficient coding as well as good programming concepts. Programming no no's will also be discussed. The concepts discussed will allow us to navigate through the trees --- that is, seeing the trees for the forest. We may have been programming in SAS for weeks, months, years or decades. Regardless, we should continue to expand our skills and continue learning and updating our techniques. With this paper, we will provide reminders for paths lost in the past, as well as new tips to help us clear the brush from the trail	proc step, in=, keep statement, proc genmod, proc sql, out=, sas programmer, temporary dataset, cpu time, raw data	10
63	Self-service utility to List and Terminate SAS grid jobs	Venkateswarlu Toluchuri	SAS programmers always have difficulty to find their submitted jobs information , and they always depend on SAS interactive client tools like SAS EG and Putty sessions to terminate them. In most cases the SAS administrators have to be involved to terminate unneeded jobs . The solution is to develop a self -service utility, so that the programmer can list and kill the jobs that are no longer required. With this approach users can i mprove overall performance of their environment and remove the dependency on the SAS administrators to kill the user jobs	sample, data step	8
64	Show Me That? Using SAS VIYA, Visual Analytics and Free ESRI Maps to Show Geographic Data	John Schmitz	Viya and Visual Analytics include features to connect with free, premium and custom ESRI maps to display geographic information. This paper provides simple examples for generating polygonal maps based on input data and free ESRI shapefiles. The paper reviews key configuration settings that impact ESRI map capabilities, generation and promotion of data for use by the geo-map feature, defining the category field for use with geo-mapping, filtering graph data, and producing choropleth style maps	sample, dataset	12
65	So You Want To Be An Independent Consultant	Joshua M. Horstman	While many statisticians and programmers are content with a traditional employment setting, others yearn for the freedom and flexibility that come with being an independent consultant. While this can be a tremendous benefit, there are many details to consider. This paper will provide an overview of consulting as a statistician or programmer. We'll discuss the advantages and disadvantages of consulting, getting started, finding work, operating your business, and various legal, financial, and logistical issues	auto insurance, current account	11
66	Spotfire Clinical visualizations from SAS and R	Zeqing Lu, Hillary Graham, Jessica Chen	Data visualization is an innovative way to help our business decision-making process more intuitive and efficient. With this emphasis in mind, our project focuses on the interactive capabiliti es of the popular visualization tools: Spotfire, SAS and R. By using all these software together, we are able to create on-demand interactive visualizations including Kaplan Meier, waterfall, and forest plots to present subgroup efficacy analyses. Exploratory analyses can be viewed before they are formalized, therefore minimal adhoc TFLs are needed, which is cost saving	forest plot, hazard ratio, subgroup analysis	4
67	Square Peg, Square Hole-Getting Tables to Fit on Slides in the ODS Destination for PowerPoint	Jane Eslinger	An output table is a square. A slide in Microsoft PowerPoint is a square. The table, being the smaller square, should fit in the bigger square slide. Right? Well, not always. Despite the programmer's expectations, some tables will not fit on the slide created by the `ODS` destination for PowerPoint. It depends on the table. For instance, tables with, say, more than 10 rows or more than 6 columns might end up spanning multiple slides. But, just as with the popular children's toy, by twisting, turning, or approaching the hole from a different angle, you can get the peg in the hole	proc step, proc report, proc freq, proc tabulate, proc genmod, proc glm, class variable, proc print	18
68	The Baker Street Irregulars Investigate: Discoveries Using Perl Regular Expressions and SAS	Peter W. Eberhardt	A true detective needs the help of a small army of assistants to track down and apprehend the bad guys. Likewise, a good SAS programmer will use a small army of functions to find and fix bad data. In this paper we will show how the small army of regular expressions in SAS can help you. The paper will first explain how regular expressions work, then show how they can be used with CDSIC	character variable, data cleaning	33
69	The Joinless Join ~ The Impossible Dream Come True Using SAS Enterprise Guide and Base SAS PROC SQL and DATA Step; Expand the Power of SAS Enterprise Guide and Base SAS in New Ways	Kent Phelps, Ronda Phelps	SAS Enterprise Guide and Base SAS can easily combine data from tables or data sets by using a `PROC SQL` Join to match on like columns or by using a `DATA STEP` Merge to match on the same variable name. However, what do you do when tables or data sets do not contain like columns or the same variable name and a Join or Merge cannot be used? We invite you to attend our exciting Joinless Join Hands-On Workshop where we will empower you to expand the power of SAS Enterprise Guide and Base SAS in new ways by creatively overcoming the limits of a standard Join or Merge. You will learn how to de sign a Joinless J oin based upon dependencies, indirect relationships, or no relationships at all between the tables or data sets using SAS Enterprise Guide and Base SAS `PROC SQL` and `DATA STEP`. In addition, we will highlight how to use a Joinless Join to prepare unrelated joinless data to be utilized by `ODS` and `PROC REPORT` in creating a PDF	proc sql, proc step, sas base, sas enterprise guide, proc report, data cleaning, inner join	18
70	Tips, Traps, and Techniques in BASE SAS for vertically combining SAS data sets	Jayanth Iyengar	Although not as frequent as merging, a data manipulation task which SAS programmers are required to perform is vertically combining SAS data sets. The SAS system provides multiple techniques for appending SAS data sets, which is otherwise known as concatenating, or stacking. There are pitfalls and adverse data quality consequences for using traditional approaches to appending data sets. There are also efficiency implications with using different methods to append SAS data files. In this paper, with practical examples, I examine the technical procedures that are necessary to follow to prepare data to be appended. I also compare different methods that are available in BASE SAS to append SAS data sets, based on efficiency criteria	proc step, proc append, proc sql, set statement, proc compare, proc contents, sas base, cpu time, dataset, sas log, proc freq, sas programmer, data cleaning, proc datasets, length statement	18
71	Tips, Tricks, and Traps on Longitudinal Data Analysis with Discrete and Continuous Times	Brandy R. Sinco, Edith Kieffer, Michael S. Spencer, Gray Ficker, Gretchen Piatt	When longitudinal data are collected at discrete time points, such as at baseline, 6 and 12 months, compared to continuous times, both exploratory data analysis and linear mixed models need to be modified. For data at discrete times, analysts can use `PROC CORR` to examine the correlation matrix by simply listing the variable names at each time point. In contrast, long datasets with continuous times must be transposed to a format that can be used with `PROC CORR`, by using the first and last functions. This presentation includes tips and tricks for viewing the empirical correlation structure when time is continuous. When using `PROC MIXED` for a linear mixed model, some covariance structures differ between models with discrete and continuous times. SAS offers covariance structures especially designed for continuous data, as well as structures that were designed for data with discrete times. One trap and trick is the Estimate statement in `PROC MIXED`	correlation, covariance, proc step, covariance structure, proc mixed, compound symmetry, proc reg, sample, proc corr, regression analysis, random intercept, unstructured covariance, confidence interval, spaghetti plot, fixed effects, proc sgplot, standard deviation, output delivery system, longitudinal data, proc plm, repeated measures, toeplitz covariance, heteroscedasticity, proc means	20
72	Toward Adoption of Agile Software Development in Clinical Trials	Troy Martin Hughes	Agile methodologies for software development, including the Scrum framework, have grown in use and popularity since the 2001 Manifesto for Agile Software Development. More than having obtained ubiquity, Agile has demonstrably defined software development in the 21st century with its core foci in collaboration, value-added software, and flexibility gained through incremental design, development, and delivery. Although Agile principles can easily be extrapolated to other disciplines, Agile-related nomenclature, literature, application, and employment descriptions often remain focused on software development alone. The clinical trials/pharmaceutical industry often embodies data analytic development- software development in which the ultimate business value is de rived not from software but from data products and data-driven decision making	clinical trial, pharmaceutical industry, macro definition, clinical data, clinical research, statistical programmer, clinical sas, sas programming, sample	7
73	Understanding Administrative Healthcare Datasets using SAS programming tools	Jayanth Iyengar	Changes in the healthcare industry have highlighted the importance of healthcare data. The volume of healthcare data collected by healthcare institutions, such as providers and insurance companies is massive and growing exponentially. SAS programmers need to understand the nuances and complexities of healthcare data structures to perform their responsibilities. There are various types and sources of administrative healthcare data, which include Healthcare Claims (Medicare, Commercial Insurance, & Pharmacy), Hospital Inpatient, and Hospital Outpatient. This training seminar will give attendees an overview and detailed explanation of the different types of healthcare data, and the SAS programming constructs to work with them. The workshop will engage attendees with a series of SAS exercises involving healthcare datasets	proc step, proc freq, sas programmer, proc sort, proc tabulate, insurer, health insurance, proc format, proc sgplot, proc sql, deduplication, group by, service provider, sample, proc contents, proc report, sas programming, by group processing, dataset, proc means, lookup	24
74	Use of SAS Macros to automate the Production of Statistical Process Control Charts	Lynn (Xiaohong) Liu, Roderick Jones	The `SAS/QC` `SHEWHART` procedure generates statistics and statistical process control (SPC) charts used to measure improvement and special cause variation in a process. To produce output automatically on a repeating schedule for a large number of metrics, a system of sequential SAS programs with macro processing was developed and implemented. The foundation for the process is a parameter file, which stores information including each metric's definition, record-level data set, variable name and label, temporal unit of analysis, starting point of the time period to be analyzed and SPC chart type. The parameter file is imported and each row (or 'run') is converted into macro va riables using `%LET` and %SYSFUNC. Moreover, CALL SYMPUT, SQL SELECT INTO and %SYMEXIST assign or detect macro variables in real time, which allows a dynamic response from the system	macro variable, proc step, macro loop, do loop, %if-%then-%else, macro function, date variable, proc means, datetime20., &variable, proc sql, datepart, macro definition	20
75	Using Multilabel Formats with PROC SUMMARY to Generate Report Data with Overlapping Time Segments	John Schmitz	SAS introduced the multi-label format (MLF) in Version 8. Yet, few users are familiar with the MLF or its unique capabilities. MLFs are used for data summarization where the same observation may be classified into 2 or more levels, simultaneously. This paper shows how multi-label formats can be used to generate time segments with overlapping periods. Core steps include creating the multi-label format definition, applying MLFs to CLASS variables within `PROC SUMMARY`, and properly understanding the	proc means, proc step, proc format, do loop	11
76	Using SAS to Validate Clinical Prediction Models	Xiaoting Wu, Chang He, Donald S. Likosky	Model validation is an important step in establishing a clinical prediction model. Model validation process quantifies how well the model predicts outcomes for future patients. However, there were very few SAS programming examples showing the validation process. We previously developed a generalized mixed effect model that predicts peri-operative blood transfusion from patient characteristics. In this paper, we demonstrate the SAS techniques that we used to validate such a model. These validation methods include calibration, discrimination and sensitivity analysis using bootstrapping method	sample, data cleaning, bootstrap, sensitivity analysis, model validation, proc step	8
77	Using SASv9.cfg, autoexec.sas, SAS Registry, and Options to Set Up Base SAS	Peter W. Eberhardt	Are you frustrated with manually setting options to control your SAS Display Manager sessions but become daunted every time you look at all the places you can set options and window layouts? In this paper, we look at various files SAS accesses when starting, what can (and cannot) go into them, and what takes precedence after all are executed. We also look at the SAS Registry and how to programmatically change settings. By the end of the paper, you will be comfortable in knowing where to make the changes that best fit your needs	proc step, libname statement	10
78	Using your FREQ effectively: Displays to Decipher Proportional Odds in Ordinal Regression	Robert G. Downer	The proportional odds assumption of the cumulative logit model is an intriguing challenge in the modeling of an ordinal response. Valuable insight for modeling decisions can be gained by further investigation of why the proportional odds assumption has been satisfied or not. This investigation can be exploratory and completely separate from the logistic modeling. Empirical cumulative logit plots are one possibility but the interpretation is not intuitive with respect to odds or proportions. This paper presents exploratory methods which enhance the toolbox for understanding the proportional odds test. In conjunction with other SAS procedures, effective tabular and graphical options of `PROC FREQ` are used to support the findings of the proportional odds test. An informal application of the Breslow Day Test is introduced. Model development is not a focus of the paper but some `PROC LOGISTIC` details are discussed	proc step, proc freq, logistic regression, proc logistic, logistic distribution, odds ratio, confidence interval, sample size, sample, regression analysis	11
79	Visual Storytelling - The Art of Communicating Information with Graphics	Kirk Paul Lafler	Telling a story with facts and data alone can be boring, while stories told visually engage. It's been said that humans tend to process visual elements many times faster than reading words. The data analysis process involves the gathering and collection, cleansing, transforming, modeling and storytelling of data from various sources. The objective is to discover, evaluate, understand and derive useful information from the data to support decision-making. Unfortunately, data analysts sometimes omit a very crucial step - the development of a visual narrative about the data analysis process and outcome. This omission not only fails to bring context, insight and interpretation of the data analysis results in a clear and precise way, it neglects to bring meaning, relevance and interest to the "key" points of the data analysis results	bar chart, scatter plot, proc sgpanel, histogram, proc sgplot, box plot, vertical bar chart, proc freq, bubble plot, data science, sas base, class variable, correlation	16
80	What's the Difference? Using the PROC COMPARE to find out.	Larry Riggen	We are often asked to determine what has changed in a database. There are many tools that can provide a list of before and after differences (e.g. redgate SQL Data Compare), but SAS `PROC COMPARE` can be coupled with other tools in base SAS to analyze the changes. This paper will explore using the output file produced by `PROC COMPARE` and the SAS Macro language to produce spreadsheets of detailed differences and summaries to perform this task	proc step, proc compare, proc contents, numeric variable, proc sql, character variable, macro variable, proc freq, binary variable, macro facility	19
81	Wow! You Did That Map With SAS?! Round II	Louise S. Hadden	This paper explores the creation of complex maps with SAS software. This presentation incorporates explores the wide range of possibilities provided by `SAS/GRAPH` and polygon plots in the SG procedures, as well as replays, overlays in both `SAS/GRAPH` and SG procedures, and annotations including Zip Code level processing. The more recent GfK maps now provided by SAS, that underlie newer SAS products such as Visual Analytics as well as traditional SAS products, will be discussed. The pre-production `SGMAP` procedure released with Version 9.4 Maintenance release 5 will be discussed in context	proc step, proc gmap, proc sgplot, sample, sas base, dataset, macro variable, proc sql	30

Back to Conf go 2019 go 2017

This site serves as a convenient hosting location and does not work as a publisher. All copyrights belong to their respective owners.

The information on this site is provided for general reference purposes only. Whilst every effort is made to ensure that the site is up to date and accurate, no warrant, nor any responsibility or liability for, the accuracy or completeness of the content or for any loss which may arise from reliance on information contained in this site is provided. Links to other sites are provided for your convenience but no responsibility or liability for the content of those sites or of any external site which links to this site is accepted. References to any specific commercial product, process, service, or corporation are also for information only, and do not constitute endorsement, recommendation or favoring.

SAS is a registered trademark of SAS Institute Inc. SAS, Statistical Analysis System, and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.