SCSUG 2012 Papers - Index

37 papers. Source: SCSUG (South-Central SAS Users Group) conference proceedings.

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	A Cup of Coffee and Proc FCMP: I Cannot Function Without Them	Peter W. Eberhardt	How many times have you tried to simplify your code with LINK/RETURN statements? How much grief have you put yourself through trying to create macro functions to encapsulate business logic? How many times have you uttered "If only I could call this `DATA STEP` as a function"? If any of these statements describe you, then the new features of `PROC FCMP` are for you. If none of these statements describe you, then you really need the new features of `PROC FCMP`. This paper will get you started with everything you need to write, test, and distribute your own "`DATA STEP`" functions with the new (SAS 9.2) `PROC FCMP`. This paper is intended for beginner to intermediate programmers, although anyone wanting to learn about `PROC FCMP` can benefit	proc fcmp, proc step, sas log, return, put statement, sas programmer	source	16
2	A SAS MACRO for estimating bootstrapped confidence intervals in dyadic regression models.	Robert Wickham	The actor-partner interdependence model (APIM; Kenny, Kashy, & Cook, 2006) is a popular procedure for ana- lyzing dyadic data (e.g., married couples, twin-siblings). Recent work (Kenny & Ledermann, 2010; Wickham & Knee, 2012) demonstrates that the ratios of regression coefficients generated by the APIM are often of substan- tive interest to researchers. Unfortunately, the sampling distributions for these ratios are non-normal, which ren- ders standard parametric tests of significance (e.g., t-test) unreliable. This talk presents a SAS MACRO for per- forming a non-parametric bootstrapping procedure that will provide unbiased confidence intervals for several of the key coefficient ratios estimated by the APIM. Extensions to this general model are discussed	confidence interval, sample, regression analysis	source	6
3	An analysis of customer preference of automobile products using SAS.	Akkarapol Sa-ngasoongsor	This paper presents an analy sis of customer preference of automobile products in terms of Willingness -To-Pay (WTP) of vehicle attributes using SAS , includin g customer segmentation , and dimensionality reduction of WTP vector. This analysis addresses issues related to the preparation of customer inputs for effective predicti on of customer preferences. Our analysis shows that respondents show maximum variabilit y in specifying their WTP for Body Type (BT) attribute and hence classification based on BT associations is the most suitable way to differentiate between respondents fr om available data. In case of dimensionality reduction of WTP vector, we propose a twofold criteria for WTP rankings. The first addresses the importance of WTP for an attribute level from a respondent's utility maximization standpoint. The second criteria addresses the predictability of WTP from the available data using Signal-to-Noise Ratio (SNR)	factor analysis, dimensionality reduction, customer segmentation, proc princomp, correlation, discriminant analysis, latent variable, scatter plot, scree plot, r-squared	source	10
4	An Introduction to SAS Hash Programming Techniques	Kirk Paul Lafler	SAS users are always interested in learning techniques that will help them improve the performance of table lookup, search, and sort operations. SAS software supports a `DATA STEP` programming technique known as a hash object to associate a key with one or more values. This presentation introduces what a hash object is, how it works, the syntax required, and simple applications of it use. Essential programming techniques will be illustrated to sort data and search memory-resident data using a simple key to find a single value	hash table, lookup, sas log, composite key, proc sort	source	8
5	Application of SAS in Product Testing in Retail Business	Steven Yan	Testing new products is an important and continuous task for the retail business. A reliable and efficient procedure to evaluate new products is critical to the success of any retailer: Either under- or over- assessment of a product may result in a significant cost and loss to the company. However, in practice many tests are much more difficult to utilize than they first appear because of various sources of uncertainty involved. For example, most tests are carried out only in a small sample of stores due to store space limitations, labor cost, product availability, and other logistic factors. In addition, store sales are affected by internal and external factors such as product inventory, weather, local holidays, etc. In this paper we will introduce a product testing procedure that we have developed using SAS that includes product segmentation, store selection, and product scoring. We will illustrate and evaluate the procedure by a simulation study	inferential statistics, standard deviation	source	7
6	Automating the Creation of Complex PowerPoints	Julie VanBuskirk	Th process. E and paste number o Office (AM SAS store document this new p productio Th refreshed processes custom st including solutions process. The Basic The first s this proce code that work thro	excel, macro variable	source	13
7	Basic SAS PROCedures for Producing Quick Results	Kirk Paul Lafler	As IT professionals, saving time is critical. Delivering timely and quality looking reports and information to management, end users, and customers is essential. The SAS System provides numerous "canned" PROCedures for generating quick results to take care of these needs ... and more. Attendees learn how basic SAS PROCedures such as PRINT and FORMS produce detail output; CHART, FREQ, MEANS, PLOT, SQL, and UNIVARIATE summarize and create graphical, tabular, and statistical output; and utility PROCedures to manage data libraries; Additional topics include techniques for informing the SAS System which data set to use as input to a procedure, how to subset data using a WHERE statement (or WHERE= data set option), and how to perform BY- group processing	proc step, proc means, numeric variable, proc sort, libname statement, proc freq, proc univariate	source	17
8	Dirty Data? Clean it up with SAS.	Gabriela Cantu	Clinical trials data can be complex and integrate multiple data elements including demographic, laboratory, clinical, medication, and medical history. Although extremely valuable to the study the completeness and cleanliness of clinical trials data is often less than ideal. In order to be successful, clinical data managers must strategize methods to maintain data integrity and cleanliness. This presentation will focus on planning for and performing clinical trials data edit checks, cleaning and documentation. Through a comprehensive planning process and a series of simple SAS procedures, dirty data can be transformed into usable and clinically informative datasets. A simple ARRAY can be used to reassign tricky variables into more useful formats. Utilization of `PROC UNIVARIATE` to produce continuous variable statistics allows data managers to identify out of range and unexpected values for clinical data	clinical trial, clinical data, proc step, missing value, nominal variable, interval variable, proc freq, proc univariate, proc print, where statement, macro definition, excel, outlier, proc sort, sas enterprise guide	source	11
9	Enhansing SAS output with Output Delivery System (ODS)	Hemalkumar Mehta	Presentation	output delivery system, proc step, dataset, sas output, ods rtf, excel, sas base, proc template, proc print, ods html	source	30
10	Enterprise Guide for New and Experienced SAS Users	Steve Fleming		proc step, sas enterprise guide	source	13
11	Exploring DICTIONARY Tables and SASHELP Views	Kirk Paul Lafler	SAS users can quickly and conveniently obtain useful information about their SAS session with a number of read-only SAS data views called DICTIONARY tables or SASHELP views. At any time during a SAS session, information about currently defined system options, libnames, table names, column names and attributes, formats, indexes, and more can be accessed and captured. This paper explores the purpose of DICTIONARY tables and views, how they are accessed, and what information is available to SAS users. Attendees learn how these important tables and views can be accessed and applied using real-world scenarios	dictionary tables, proc sql, proc step, options statement, sas library, select	source	7
12	Google Search Tips and Techniques for SAS and JMP Users	Kirk Paul Lafler	Google (www.google.com) is the worlds most popular and widely-used search engine. As the premier search tool on the Internet today, SAS and JMP users frequently need to identify and locate SAS and JMP content wherever and in whatever form it resides. This paper provides insights into how Google works and illustrates numerous search tips and techniques for finding articles of interest, reference works, information tools, directories, PDFs, images, current news stories, user groups, and more to get search results quickly and easily		source	5
13	Identical SAS IntrNet code	Rob Caudill	This paper is about identical `SAS/INTRNET` code for test and production environments	macro variable, sample	source	3
14	Institutional Research Intelligent: Go Beyond Reporting	Harjanto Djunaidi	Recent dynamic changes in the economy, government funding regulations and education industry have forced many institutions to operate with a tight if not a smaller budget. Some schools have to freeze faculty's and staff's salary increase for many years in order to stay afloat. Others have to reduce students' services and at some points also lower the quality of delivered services to the students by increasing the class size. In order to cope and adapt with the new realities, colleges have no other choice, but to increase their operational efficiency. The Institutional Research Intelligence (IRI) is a new concept that provides tools to help administrators such as the President, VPs, Deans, Department Heads, Program Directors and other school decision makers to achieve the institution's goals more effectively. Given the significant and dynamic changes in the industry, universities and colleges in the US cannot just operate BAU (business as usual)	regression analysis, logistic distribution	source	9
15	JavaScript menu that submits SAS/IntrNet Report code	Rob Caudill	This paper is about a JavaScript menu that submits `SAS/INTRNET` Report code		source	3
16	Keep it Organized: A Grad Student "How To" Paper	Elisa L. Priest	Grad students learn the basics of SAS programming in class or on their own. The classroom lessons focus on statistical procedures and the datasets are usually ready for analysis. However, independent research projects may have data organized in many complex structures and may require many different SAS programs for data organization, exploration, and analysis. At the end of a project, you generally end up with multiple SAS programs and data sets. During the project, students may not realize the need to organize and document their project. However, it may become painfully clear if modifications need to be made to the completed analysis. This paper will provide students with the tools that can help them survive the challenges of organizing and a documenting SAS code for a research project. We will present examples to help students keep their SAS code and data sets organized using base SAS as well as Enterprise Guide 5.1	sas enterprise guide, sas base, adam dataset, in=, proc step	source	6
17	MULTI-PANEL SCATTER PLOTS AND SCATTER PLOT MATRICES	Paulina Kulesz	A scatterplot is one of the most common tools utilized in the visual exploration of data. The scatter plot aids researchers in examination of relations between two variables X and Y, as well as reveals degree of symmetry, concentration of data, and possible outliers. A bivariate scatter plot is the simplest form of representing a relation between two variables. More advanced forms such as panel scatter plots and scatterplot matrices allow representing a degree of dependence between multiple variables (taken two at the time) in a comparative way. This paper presents improvements in visualizing data utilizing variations of scatterplots. More specifically, it presents how to create analytical graphs representing bivariate relations between variables in SAS. The SGPANEL and SGSCATTER procedures supporting different statements and options including annotation (SGANNO) and attribute maps (DATTRMAP) are utilized in order to generate graphs useful in enhancing the visual exploration of data	scatter plot, proc sgpanel, proc sgplot, macro definition, histogram, proc step	source	13
18	Nesting Multiple Box Plots and Blockplots using GTL and Lattice Overlay	Greg Stanek	There are times when the objective is to provide a summary table and graph for several quality improvement measures on a single page to allow leadership to monitor the performance of measures over time. The challenges were to decide which SAS procedure(s) to use and how to integrate multiple SAS procedures to generate a set of plots and summary tables within one page and to determine whether to use box plots or series plots of means or medians. We considered the `SGPLOT` and SGPANEL Procedures and Graph Template Lange	box plot, proc means, proc step	source	13
19	Net Present Value Model Approach	Jingjing Qu		logistic regression, regression analysis, logistic distribution, likelihood function, survival analysis, relative risk, survival function, proc phreg	source	7
20	Output Delivery System (ODS) - Simply the Basics	Kirk Paul Lafler	Are you looking for ways to improve or enhance the appearance of your SAS output? Output Delivery System (`ODS`) can help turn tired-looking output into great-looking information that has a purpose. Gone are the days when the only available formatting choice was the boring output listing that contained lifeless monospace fonts. `ODS` introduces exciting features that improve the appearance of your output. Using built-in format engines, `ODS` provides SAS users with a powerhouse of exciting capabilities to produce publishing-quality output. This presentation provides an introduction to `ODS`, including basic operation and syntax; how specialized output can be created, including RTF, PDF, HTML, Microsoft `EXCEL` spreadsheets, and SAS data sets; and how selection (or exclusion) lists can be constructed to handle basic content customizations	proc univariate, output delivery system, sas output, ods pdf, ods html, ods rtf, dataset, proc step	source	14
21	Overlaying Scatter Plots on Box Plots in the Presence of Ties	Charles Minard	Graphical methods are important for efficient and effective visualization of data. Many graphical methods are available, and combining different types of graphics can yield interesting and effective results. Box plots are commonly used to compare the distributions of continuously measured variables across two or more groups, and overlaying a scatter plot on a box plot can provide additional information about distributional differences or similarities. However, ties may be common when the response variable is not measured with high fidelity such as when time is measured in days as an integer value. Random jitter could be used to create separation between points when ties are present in the response variable. However, this can create a figure that is distorted and somewhat difficult to interpret. Evenly distributing ties across the horizontal scale creates a figure that is clearer and more informative	box plot, scatter plot, class variable, response variable, proc sort, proc step	source	11
22	Picture Perfect Graphing with Statistical Graphics Procedures	Julie VanBuskirk	Do you have reports based on `SAS/GRAPH` procedures, customized with multiple GOPTIONS? Do you dream of those same graphs existing in a GOPTIONS and Annotate free world? Recreating complex graphs using Statistical Graphics (SG) procedures is not only possible, but much easier than you think! Using before and after examples, I will discuss how the graphs were created using the combination of `PROC TEMPLATE`, Graph Template Language (GTL), and the SG procedures. This method produces graphs that are nearly indistinguishable from the original, with code that has been proven to be reusable across projects, and is based on one central style template which allows style changes to cascade effortlessly. I will describe how we were able to mimic the customization of `SAS/GRAPH` using the combination of `PROC TEMPLATE`, Graph Template Language (GTL), and the SG procedures	horizontal bar chart, bar chart, proc step, proc template	source	14
23	POSTER: ADaM Implications of the CDER Data Standards Common Issues Document	Sandra Minjoe	Over the past few years, the United States Food and Drug Administration (USFDA), specifically the Center for Drug Evaluation and Research (CDER), has been receiving more data from sponsors in a Clinical Data Interchange Standards Consortium (CDISC) or CDISC-like structure. Reviewers have had tools built and received training, but there are some technical issues with many submissions that are hindering their review process and thus their full adoption of CDISC. This prompted the issuance of a document entitled "CDER Common Data Standards Issues Document". Working closely with CDER to address many of these issues, the CDISC Submission Data Standards (SDS) team created an Amendment to the Study Data Tabulation Model (SDTM) version 1.2 and the SDTM Implementation Guide (IG) version 3.1.2. The CDISC Analysis Data Model (ADaM) team has not created a corresponding amendment, but there are many issues noted in the CDER document that have implications on ADaM	study day, adam dataset, imputation, adverse events, baseline characteristics, sdtm domain, cdisc	source	6
24	Predicting Students Enrollment Using SAS	Harjanto Djunaidi	Data-driven strategic decisions have been utilized and used many years in other areas than in education. For example, most financial or manufacturing companies have used simulation, mathematical programming and statistical approaches to improve their ability to make sound strategic investment and resource allocation decisions. Recent dynamic changes in the US and global economy, federal government financial assistance regulations as well as in the competitive environments where higher education institutions are operating have forced colleges and universities in the US to find new operational paradigms which can be applied to optimize their resources. The Institutional Research Intelligence (IRI) is the next needed concept which offers future tools to provide vital information to the administrators. One possible application of the IRI concepts is to use statistical analyses such as logistic regression model to predict student yield	logistic distribution, regression analysis, logistic regression	source	9
25	SAS Data Management Techniques: Cleaning and Transforming Data for Delivery of Analytic Data Sets	Christopher W. Schacherer	The analytic capabilities of SAS software are unparalleled. Similarly, the ability of the Output Delivery System to produce an endless array of creative, high-quality reporting solutions is the envy of many analysts using competing tools. But beneath all of the glitz and glitter is the dirty work of cleaning, managing, and transforming raw source data and reliably delivering analytic datasets that accurately reflect the processes being analyzed. Although a basic understanding of `DATA STEP` processing and `PROC SQL` is assumed, the present work provides examples of both basic data management techniques for profiling data as well as transformation techniques that are commonly utilized in the creation of analytic data products. Finally, examples of techniques for automating the generation and delivery of production-quality, enterprise-level datasets are provided	missing value, proc step, input statement, proc sql, macro variable, character variable, user-defined format, data dictionary, substr function, raw data, data governance, proc format, date variable, data transformation, dataset, data cleaning, sysdate9, index, numeric variable, put statement, automatic variable, if-then-else, retain, proc freq, audit dataset, nominal variable, datetime variable, class variable, mdy, record count, sas programmer, macro facility, string functions, sas log, excel, datepart, proc printto	source	38
26	SAS Dates: Facts, Formats, and Functions	Debbie Buck	Among the first features of SAS that users learn is that SAS dates (and times) have unique characteristics. A SAS date isn't a "standard" numeric or character variable - when displayed it can look like character data but is stored as a number. The date of January 1, 1960, has a value of zero (0) - prior dates are negative numbers, those after have positive values. Despite SAS dates being part of the initial learning curve, there are a number of factors concerning dates that are frequently less than obvious to users, even experienced users. In this presentation, we focus on displaying/outputting SAS dates (formats), reading dates (informats), importing/exporting dates, calculating intervals and differences (functions), and extracting or combining portions of dates (more functions)	sysdate9, date variable, sas log, excel, dataset, input statement, character variable, proc step	source	8
27	SAS HASH Programming Basics	Daniel Sakya	HASH programming is a hot topic in the industry that started with SAS 9. This paper is intended to provide more exposure to novice or experienced SAS programmers that are looking for alternatives to `DATA STEP` programming. The concept of HASH programming is similar to the definition of an array in SAS. Several SAS users have benefitted from HASH programming by considerably reducing the processing time for compound data merging tasks. This paper will entail the introduction to HASH programming, its syntax, a few examples, and last but not least, the benefits on using HASH programming versus regular `DATA STEP`. The examples and syntax illustrated are in SAS 9.2	hash table, data step	source	6
28	SAS Programming Tips, Tricks and Techniques	Kirk Paul Lafler	The base-SAS System offers users the power of a comprehensive `DATA STEP` programming language, an assortment of powerful PROCs, a macro language that extends the capabilities of the SAS System, and user-friendly interfaces including the SAS Display Manager. This presentation highlights numerous SAS tips, tricks and techniques using a collection of proven code examples related to effectively using the SAS Display Manager and its many features; process `DATA STEP` statements to handle subroutines and code libraries; deliver output in a variety of formats; construct reusable code; troubleshoot and debug code; and an assortment of other topics	proc univariate, proc step, options statement, proc sql, dataset, macro facility, macro variable, macro parameter, %macro parameters, sas log, dictionary tables, ods pdf, ods rtf, sas output, if-then-else, ods html, macro definition	source	16
29	The Effect of Adding the Chronic Disease Score to the Charlson Comorbidity Score in Predicting Mortality in Type - II Diabetes Patients: An Application of Reclassification Measures	Hemalkumar Mehta	Risk adjustment models such as Charlson Comorbidity Score (CCS) and Chronic Disease Score (CDS) are used to control for confounding and predicting outcomes in epidemiologic studies. A traditional statistical measure such as concordance statistics has been used widely in literature for comparison of different risk adjustment models. Recently, new measures have been introduced for comparison of such models, including reclassification methods such as reclassification tables and calibration statistics, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). In the current study, in addition to c-statistics, we show the application of novel reclassification measures in comparing risk adjustment models. We compared CCS and CCS + CDS models in predicting one-year mortality in type 2 diabetes mellitus patients using the Clinical Practice Research Database (CPRD) database. Descriptive statistics was used to describe the cohort	proc means, risk adjustment, logistic distribution, proc step, correlation, logistic regression, p-value	source	17
30	The Good, The Bad, and The Ugly	Toby Dunn, Kirk Paul Lafler	The SAS System has all the tools users need to read data from a variety of external sources. This has been, perhaps, one of the most important and powerful features since its introduction in the mid-1970s. The cornerstone of this power begins with the INFILE and INPUT statements, the use of a single- and double-trailing @ sign, and the ability to read data using a predictable form or pattern. This paper will provide insights into the INFILE statement, the various styles of INPUT statements, and provide numerous examples of how data can be read into SAS with the `DATA STEP`	input statement, raw data, infile statement, dataset, missing value, sas log, trailing @	source	12
31	The SAS Data Step: Where Your Input Matters	Peter W. Eberhardt	Before the warehouse is stocked, before the stats are computed and the reports run, before all the fun things we do with SAS can be done, the data need to be read into SAS. A simple statement, INPUT, and its close cousins `FILENAME` and INFILE, do a lot. This paper will show you how to define your input file and how to read through it, whether you have a simple flat file or a more complex formatted file	input statement, infile statement, raw data, sample, date variable, character variable, data step, sas log, cpu time, delimited file, length statement, dataset	source	18
32	The SAS Programmer's Guide to XML and Web Services	Christopher W. Schacherer	Because of XML's growing role in data interchange, it is increasingly important for SAS programmers to become familiar with SAS technologies and techniques for creating XML output, importing data from XML files, and interacting with web services-which commonly use XML file structures for transmission of data requests and responses. The current work provides detailed examples of techniques you can use to integrate these data into your SAS solutions using XML Mapper, the XML `LIBNAME` engine, Output Delivery System, the `FILENAME` statement, and new SOAP functions available beginning in SAS 9.3	web service, libname engine, dataset, proc step, macro variable, proc print, insurance claims, proc template, sas library, output delivery system, sas programmer, filename statement, libname statement, ods html, missing value, proc sql, excel, macro quoting function	source	32
33	Two Methods to Merge Data onto Every Observation in Another Dataset	Lisa A. Mendez	here are t scenario w more than One metho utilizes Pro thing. Pers	flag variable, standard deviation, proc sql	source	11
34	Use and Applications of JMP in Microbial Ecology	Jose Garcia-Mazcorro		proc mixed, sample, hierarchical clustering, predictor variable	source	17
35	Using SAS to Locate and Rename External Files	Lu Gan	When reading in external data into SAS, a program needs to specify the data file location and file name in the related importing statements. If the raw data file is named with a `TIMESTAMP`, the import program will require an extra step which is updating the file name every time before it's run. This will be a hassle if the import is needed on a routine schedule and/or there are multiple external data files. Therefore, a SAS program can be written to eliminate the repeating manual update. This approach includes two steps, first searching for the latest data file in the specific file directory, then renaming it by removing the specific `TIMESTAMP`. After execution of the above steps, the import program will be able to read in the data files directly. The examples and syntax illustrated are in SAS 9.2. This paper assumes that the reader has a basic understanding of `DATA STEP` programming and the macro language	dataset, macro variable, %if-%then-%else	source	11
36	Using SAS to Read From And Write To EXCEL Workbooks Set Up as Templates Which Are Not Set Up In Columns	Carl Raish		dataset, excel, name range list, data step, macro variable	source	57
37	When was my data last updated? How to automate data monitoring and notification.	Drew Turner	Knowing when data was last updated can prove invaluable when verifying data integrity. If you have ever spent time troubleshooting a process only to find out that your root problem was outdated data, then you know the importance of keeping track of how recent your data is. Having that information about your data-how much there is and how recently it was updated-awaiting you first thing in the morning increases efficiency, and the ability to analyze and compare past results enables you to recognize trends and better predict future activity. This paper will describe a method to identify when a table was last updated (whether stored in a separate database or the SAS server), how to store that information for later analysis and finally how to automatically notify you of the results	proc step, macro variable, oracle, proc contents, proc sql	source	10

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