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BUGSWUSS 2006 Papers - Index
91 papers. Source: WUSS (Western Users of SAS Software) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | "Table 1" in Scientific Manuscripts; Using PROC REPORT and the ODS System | Carter Sevick | The ability to combine the power and flexibility of PROC REPORT and the Output Delivery System (ODS) has revolutionized summary reporting and table creation. However, the majority of resources available approach the subject predominantly from a business perspective. Researchers in public health tend to emphasize categorical data over continuous and have a different aesthetic when it comes to overall look and design creating the need for a subtly different skill set and approach. This paper will take the reader through the process of creating a summary table from raw data and presenting the results in RTF format. Summarized data will include a binary exposure stratified by demographic information, and in addition to frequencies, univariate analyses will also be presented. It should be emphasized that this paper is not a discussion of a study, but rather a discussion of data presentation. It will be assumed that the reader has beginning or intermediate experience with SAS. |
proc report, proc step, chi-square test, output delivery system, ods rtf, proc print | source | 19 |
| 2 | A Close Look at Numeric Formats | Vijay Kumar | Working with numeric formats can be tricky . SAS behaves di fferently with the values that have length more than specified in format, when it comes to numeric formats. T his paper discusses the numeric formats with examples and an approach to handle these cases. | numeric variable, put statement | source | 3 |
| 3 | A Dashboard Application Developed Using SAS GRAPH Proc Annotate | Timothy A. Kenney | For the web-based 2005 National Healthcare Quality Report State Resources, we developed a simple speedometer application using PROC Annotate and the macro %CYARROW (SUGI 30, '%CYARROW, A NEW ANNOTATE MACRO" Chung and Huang) to graphically demonstrate "the State's performance on summary measures of the quality of types of care, settings of care, and overall quality of health care relative to the region or Nation on each summary measure." (http://www.qualitytools.ahrq.gov/qualityreport/2005/state/summary/intro.aspx). This paper discusses the process we used to narrow our development tools down to SAS/GRAPH and PROC ANNOTATE, the steps required to move from data to graphics using standard tools available in Microsoft Windows XP PRO and SAS, the development steps associated with shaping a final graphic, and the issues we faced in publishing the results in a web based format. The final process used the MACRO language, the SAS/GRAPH ANNOTATE procedure, and the BASE product. |
proc step, proc means, sample | source | 8 |
| 4 | A Hands-on Tour Inside the World of PROC SQL | Kirk Paul Lafler | Structured Query Language (PROC SQL) is a database language found in the base -SAS software. It enables access to data stored in SAS data sets or ta bles using a powerful assortment of statements, clauses, options, functions, and other language features. This hands -on workshop presents core concepts of this powerful language as well as its many applications, and is intended for SAS users who desire an overview of the capabilities of this exciting procedure. Attendees will explore the construction of simple SQL queries, ordering and grouping data, the application of case logic for data reclassification, the creation and use of views, and the construction of simple inner and outer joins. |
proc sql, where statement, proc step, inner join, select, index, view, data merging, order by, right join | source | 11 |
| 5 | A Pragmatic Programmers Introduction to Data Integration Studio: Hands on Workshop | Gregory S. Barnes Nelson | ETL is the process of moving data from a source system (such as operational systems or a table in a database) into a structure that supports analytics and reporting (target). This workshop will guide participants through a structured, hands-on exercise designed to give them a broad overview of what things we can accomplish with Data Integration Studio. Here we will prepare data for use by extracting data from an external file, creating transformations that enrich our data, combining it with other data for completeness and finally loading the data into tables that are part of a star schema. The goal of this workshop will be to get users comfortable with the tool and demonstrate its capability. | sas data integration studio, libname statement | source | 36 |
| 6 | A Researcher's Guide to Making Descriptive and Analytic Tables that are Ready to Publish | Kathryn Martin | Tables showing cross-tabulated frequencies of many covariates by a categorical outcome and corresponding chi- square tests are often the first tables presented in articles published in health-related peer-reviewed journals. Descriptive and analytic tables, such as these, are useful to examine crude associations between many covariates and a categorical variable. Researchers go through many versions of tables while conducting analyses and preparing to publish, necessitating automated procedures that assist in analyzing data and presenting results in formatted tables. Although methods such as the Output Delivery System (ODS) have made it easier to transfer SAS data to Microsoft EXCEL, researchers still spend large amounts of time preparing tables for presentation. |
macro variable, excel, dataset, proc freq, proc step | source | 10 |
| 7 | Advanced PROC Report: Traffic Lighting - Controlling Cell Attributes With Your Data | Arthur L. Carpenter | Color or shading is often used in a report table to attract the reader's eye to specific locations or items. The color can be fixed or it can be data dependent. Of course when the color or shading is dependent on the value presented in a given cell, manual construction of the table can be very tedious. Fortunately, with the advent of the Output Delivery System, a number of new capabilities have become available to the SAS programmer. These include the ability to automatically control the color or shading of a particular cell of the table, and to even base that control on the data itself. The control of cell attributes, such as foreground and background color, is known as traffic lighting. While traffic lighting can be performed on tables generated by TABULATE and PRINT as well, PROC REPORT has the more sophisticated capabilities. The CALL DEFINE statement is only available in the REPORT step's compute block, and the STYLE= option is more complex. |
proc step, proc report, proc format, output delivery system, if-then-else | source | 16 |
| 8 | Advanced Warehousing with SAS9 | Gary Mehler | The world of data warehousing, data marts, and data integration continues to progress as more complex Business Intelligence environments become more widespread. This paper discusses the new and improved capabilities of SAS Data Integration Server (formerly known as SAS ETL Server) and SAS9 in general, to meet these challenges. These capabilities are discussed in the context of meeting specific business challenges and include the areas of administration of complex systems, metadata synchronization, ways to work with parallel execution of processes, and working with parallel storage. Monitoring during the operational phase of a warehouse is also suggested, and updated suggestions are given for multi-developer ETL environments. Armed with knowledge about these areas, warehouse developers can keep their productivity high as they address existing warehouse challenges and still have time to focus on new integration challenges as they arise. | sas grid, parallel processing, data integration, elapsed time, data standard, dataset, data wrangling, fact table, star schema, macro variable | source | 17 |
| 9 | An Approach Finding the Right Tolerance Level for Clinical Data Acceptance | Karen Walker | Highly broadcasted zero tolerance initia tives for database error involving clinic al trials are discussed ad infinitum among pharmaceutical research executives. Their case is construed as 100% audit of efficacy variables and serious adverse events to prove the qua lity of the data used in the clinical trial is good. For some clinical trials studies databases are too large to perform an audit of all the efficacy and safety vari ables. Therefore, an audit proving a large database acceptable without a structured approach may be dubious at best. For instances such as this, it is reasonable to employ a systematic approach for finding the right level of tole rance for clinical data acceptance. This approach must test the following markers: tolerance limit, error rate, edit checks, manual review, sample sizes, and a rank of sampling schemes; given the use of a statistical inference to determine how good the error rate is with regard to specific tolerance limits. | clinical database, normal distribution, crf, clinical trial, sample, proc calis, inferential statistics, data validation rules, clinical data, dataset, sample size, raw data, proc step, proc sql, adverse events | source | 5 |
| 10 | Applying SPC Methods to Improve Work Flows in High Volume Business Environment | Arkady M. Kushnir | It is become critical in high volume business environment to provide competent work flow control, efficiently schedule available resources and forecast trends to continuously improve business efficiency. One of the critical roles of the American Express Ne w Accounts processes is detection of fraudulent applications. Within inconsistent volume flows of applications for new credit CARDS and balance transfers, effective scheduling of av ailable resources has become a challenge. Within NA operations, all potentially fraudule nt applications routed into specific portfolios / work lists, where analysts perform labor intensive IDs verifications to identify potential fraud. Base SAS software were used to develop and implement Statistical Process Control Method (SPC), allowing management effectively control and forecast daily volumes of potentially fraudulent applications and improve work scheduling within NA fraud department. |
create table, proc step | source | 13 |
| 11 | Between Automation and Exploration: SAS Graphing Techniques for Visualization of Survey Data | Chong Ho Yu, Samuel DiGangi |
The objective of this presentation is to introduce SAS graphing techniques for visualization of survey data. There is always a tension between automation and exploration. Automation is a common practice in the context of large-scale data processing. When there are many variables, it is more efficient to automate the graphing processes via SAS/MACROS and SAS/GRAPH. However, hidden patterns of the data may not be revealed. On the other hand, SAS/INSIGHT, which is a tool for exploratory data analysis, requires manual manipulation of the data and thus is time- consuming. In this case study, both SAS/MACROS and SAS/INSIGHT are employed for a survey study situated Arizona State University. SAS/MACROS codes are written in a way that enables the researcher to gain some degree of manipulation and exploration. |
bar chart, histogram, proc means, proc freq, proc step | source | 8 |
| 12 | Calculating Duration via the Lagged Values of Variables | Pon Su | Duration calculation is common in many SAS programming tasks. Whenever an event occurs between a start date and an end date, one may need to compute the elapsed duration (in seconds, minutes, days) for the said event. The programming challenge in SAS often arises from having to perform operations across many observations for the same person. As most books and papers about SAS have not fully explored the LAG function in the context of duration calculation, the author now wishes to leverage the LAG function and offer a different programming | lag function, dif function | source | 4 |
| 13 | Changing Careers to Become a SAS Programmer in the Biotech/Pharmaceutical Industry | Sy J. Truong | The ebb and flow within the technology sector, ranging from the dot com bubble to outsourcing, has programmers searching for job security. Many individuals have considered becoming a SAS programmer within the Biotech and Pharmaceutical industry due to the longer project life cycle of years rather than months. The best path to enter this field can be elusive. There are challenges especially for those starting out since it requires three years of experience to obtain that first job. This paper distills real life stories from SAS programmers within the Biotech and Pharmaceutical industry. It describes many different paths and suggests recommendations based upon these experiences. When starting out on a new career path, sage advice from seasoned professionals in the field can save a lot of time and energy. Armed with the knowledge and shared experiences from the stories told in this paper, you will propel yourself into a more challenging and successful career. | sas programmer, clinical trial, sas programming, pharmaceutical industry, sas consultant, clinical data, statistical programmer, adverse events, clinical sas, data cleaning, sample, proc step | source | 9 |
| 14 | Clean Up Your Act: Data Cleaning with SAS | Mel Widawski | There is usually no such thing as a clean data set, and this includes publicly available datasets that have been used for years. There are three steps in data cleaning: 1) Discover the problem, 2) Locate the item in error, 3) Correct the error. We will explore each of these areas to discover some short cuts, guidelines, and hints. | proc step, numeric variable, interval variable, proc freq, put statement, proc print, nominal variable | source | 9 |
| 15 | Creating AND Importing Multi-Sheet Excel Workbooks the Easy Way with SAS | Vincent DelGobbo | Transferring data and analytical results between SAS and Microsoft EXCEL can be difficult, especially when SAS is not installed on a Windows platform. This paper discusses using new XML support in Base SAS 9.1 software to move data between SAS and Microsoft EXCEL (versions 2002 and later). You will learn step-by-step techniques to quickly and easily create multi-sheet EXCEL workbooks containing your SAS output. You'll also learn about converting your own multi-sheet EXCEL workbooks to SAS tables. The techniques described here can be used regardless of the platform on which SAS software is installed, including a mainframe! The use of SAS server technology is also discussed. |
output delivery system, excel | source | 20 |
| 16 | Creating and Modiying PDF Bookmarks | Tikiri B. Karunasundera | There have been multiple publications about how to create PDF files with two levels of bookmarks using PROC REPORT. There are two fundamental limitations on the bookmarks created through ODS PDF. The first is the parent bookmarks are not grouped together and the second is two levels of bookmarks may not be adequate for easy navigation. This paper suggests solutions to both issues by two simple Java Script functions and minor additions to the SAS program. |
ods pdf, group by | source | 6 |
| 17 | Creating Statistical Graphics in SAS 9.2: What Every Statistical User Should Know | Robert N. Rodriguez, Tonya E. Balan |
Effective graphics are essential to modern statistical analysis. SAS 9.1 introduced an experimental exten- sion to the Output Delivery System (ODS), which is used by over two dozen SAS/ST A T and SAS/ETS procedures to create statistical graphics as automatically as they create tables. This extension, referred to as ODS Statistical Graphics (or ODS Graphics for short), requires minimal additional syntax, and it provides displays commonly needed for data analysis and statistical modeling, including scatter plots, histograms, and box-and-whisker plots. General ODS features, such as styles and destination statements, apply equally to tables and graphs. With the production release of ODS Graphics in SAS 9.2, many more statistical procedures have been modified to use this new functionality; see Appendix A for a list. For example, PROC LOGISTIC produces effects plots, and PROC RSREG produces contour and ridge plots. |
output delivery system, proc step, ods graphics, proc reg, ods html, scatter plot, kaplan-meier plot, proc sgplot, ods rtf, regression analysis, box plot, data science | source | 32 |
| 18 | Data Driven Annotations: An Introduction to SAS/GRAPH's Annotate Facility | Arthur L. Carpenter | When SAS/GRAPH was first introduced, it was the 'only game in town' for the generation of business graphics. In the current computing environment we have lots of graphics options, and it is not unusual for programmers new to SAS/GRAPH to ask why they should bother learning the command and syntax structure of SAS/GRAPH, let alone that of the Annotate Facility. The fact of the matter is that SAS/GRAPH is still the 'only game in town' with regards to the generation of data driven graphics. When you want to create a large number of graphs (or even a single graph on a regular basis) and you do not want to place labels or other graphic enhancements manually, the Annotate Facility allows you to set up a system that places these items on the graph based on the data contained in the graph itself. In this workshop we will learn how Annotate communicates to the graphics procedures through the annotate data table. |
dataset, proc step, histogram, character variable, length statement, proc gchart, retain, numeric variable, proc gplot | source | 17 |
| 19 | Detecting Structural Change Using SAS ETS Procedures | Archie J. Calise, Joseph E. Earley |
This paper illustrates how the SAS System may be used to test for structural change in a time series. PROC AUTOREG with the CHOW option are used to perform a Chow test for structural stability on airline passenger travel time series data before and after the September 11, 2001 terrorist incident. The paper illustrates the ease of use of this procedure in investigating the structural change of a time series. Also illustrated in the paper is the Time Series Forecasting System, which is a modular option under Solutions> Analysis>Time Series Forecasting System. The TSFS may be use to fit numerous pre-selected and other models based on a user-selected criterion such as R- square or mean square forecast error. |
time series analysis, regression analysis, rmse, mse, proc calis, anova, response variable, proc autoreg, binary variable, predictor variable, proc step, residual | source | 7 |
| 20 | Dynamic Code Creation Using Call Symput and the SAS Macro Language | Mark Gustafson | Large sections of redundant code are time consuming to create, tedious to write, and prone to error. The need to write such code can often be eliminated through the use of macro programming. This paper will discuss one such method that allows the user to create code dynamically using information contained within the data being analyzed, which allows the programmer to write stock programs that are usable regardless of the data being analyzed. This is extremely useful for routine tasks, such as monthly reports, that must be run repeatedly on datasets that are constantly changing. There are numerous applications for this method, from the creation of dummy variables for regressions to analyzing different types of adverse events in a drug study without prior knowledge of what events occurred. | macro variable, binary variable, call symput, regression analysis, proc freq, adverse events, in=, proc step, macro definition | source | 4 |
| 21 | Easy Graphs with PROC FORMAT, PROC GPLOT, and ODS | Apryl DeLancey | By means of straightforward commands in SAS v8.2 and SAS/GRAPH, graphs and charts can be generated that are visually appealing and easy to understand without having to import data into another graph-generating program. Using PROC GPLOT/GHART in conjunction with PROC FORMAT and ODS (output delivery system), data can be visualized in a comprehensible style. Many times visualizing data using traditional linear graphs or pie charts is done with a product such as Microsoft EXCEL or other graphing software after data is compiled and manipulated using SAS. By using PROC GPLOT or PROC GCHART with PROC FORMAT a colorful, quality graphical representation of data can be produced. By using ODS statements, the graphs that are generated can be output to pdf (Portable Document Format) or other usable formats. The output is easily inserted into a slideshow or printed. More importantly, since the data is solely manipulated in one program, there are fewer chances for error. |
proc step, proc gplot, proc format, excel | source | 5 |
| 22 | Easy Methods to Investigate Large Datasets Using ODS Graphics and New Features in SAS 9 | Aimee Foreman, Miriam G. Cisternas |
Data analysts who took even a couple of applied statistics courses in school were probably taught that a thorough analysis begins with a graphical examination of the data. And many of us even looked at univariate and bivariate scatter plots and histograms as a precursor to our multivariable regression model-building assignments. But as we entered the professional world, the size of our data sets increased, while our available time decreased. The practice of producing a graphical display of data prior to analysis often went by the wayside in an effort to get the job done. Luckily, the availability of faster computers and new graphical features in SAS 9 (including ODS Graphics) has allowed us to easily produce graphs to aid our understanding of univariate, bivariate, and even multivariate relationships before commencing hypothesis testing or model-building analyses. |
histogram, proc step, normal distribution, numeric variable, box plot, proc univariate, standard deviation, sample, nominal variable, density plot, class variable, by group processing, data visualization, outlier | source | 10 |
| 23 | Enterprise Guide for Business Analysts | Sunil Kumar Gupta | Business Analysts will find that SAS's new Enterprise Guide makes it easier to perform data analysis a nd reporting. Using the task-oriented menus reduces the time to deliver results. This presentation shows simple step-by-s tep instructions on how real-world business-related questions can be answer ed using Enterprise Guide. With a better understan ding of product sales, you can predict trends for better planning. With Enterprise Guide, you have access to the full power of SAS's analytic capabilities without writing any SAS code. Learn SAS programming the easy way and automatically generate SAS code for your reports. This paper will feature Enterprise Guide version 3.0. This paper addresses the following topics: /head2right SAS Enterprise Guide: The Basics /head2right Selecting Analysis and Reporting Tasks /head2right Producing Reports for Distribution | sas enterprise guide, proc step, proc hpsplit, proc report, sas programming, excel, data science | source | 10 |
| 24 | Environmental Samples Below the Limits of Detection - Comparing Regression Methods to Predict Environmental Concentrations | Daniel Smith, Elana Silver, Martha Harnly |
In exposure assessment studies of trace environmental contaminants, investigators often face the difficulty of dealing statistically with chemical values below the laboratory's limits of detection. For example, in a study of pesticides measured in the dust of homes near agricultural fields, the goal is to use characteristics of the household to predict pesticide concentrations, even though many of the measurements are below the detection limit. We illustrate two regression techniques that can be useful in this situation: quantile regression and tobit regression. Both are available in SAS, as PROC QUANTREG and PROC QLIM respectively, yet have not been used extensively by epidemiologists and environmental health scientists. Quantile regression predicts a given quantile, such as the 50th or 90th percentile, rather than the mean as in standard regression, thereby sidestepping uncertainties in the dependent variable at the lower end of the distribution. |
regression analysis, proc quantreg, confidence interval, proc reg, response variable, proc step, multiple regression, sample, survival analysis, predictor variable, proc lifereg | source | 8 |
| 25 | Errors, Warnings, and Notes (Oh My) A Practical Guide to Debugging SAS Programs | Lora D. Delwiche, Susan J. Slaughter |
Errors? "I never get errors!" Warnings? "I never bother to read them, my program still runs." Notes? "Who needs them?" This paper, is based on the belief that debugging your programs is not only necessary, but also a good way to gain insight into how SAS works. Once you understand why you got an error, a warning, or a note; you'll be better able to avoid problems in the future. In other words, people who are good debuggers are good programmers. We cover common problems including missing semicolons, invalid data, truncated character values, missing-data-was- generated messages, character to numeric conversions, and the tricky problem of a DATA STEP that runs without suspicious messages but nonetheless produces the wrong results. For each problem we decipher the message, list possible causes, and explain how to fix the problem. We also cover some programming strategies for avoiding errors in the first place. |
sas log, missing value, proc print, put statement, proc step, input statement, error message, dataset, character variable, infile statement, numeric variable, firstobs, options statement, sample, excel, sas output | source | 15 |
| 26 | Essential ODS Techniques for Creating Reports in PDF | Patrick Thornton | This paper lists and briefly describes ODS techniques that I have found most useful in capturing SAS 9.1 results and creating user friendly PDF reports on a Windows operating system. The techniques are presented for the beginning SAS user with examples and references to more detailed papers. The ODS techniques include capturing one-way frequency output to a data set, creating a PDF document, changing styles, controlling page setup (e.g. orientation, date, title, and footnote), using PUT, and formatting and traffic-lighting PROC REPORT. |
proc report, proc step, proc freq | source | 6 |
| 27 | Examples of Data Manipulation in SAS: Filling in "Known" Missing Data | Chong Ho Yu | On many occasions analysts encounter the problem of missing data. The objective of this presentation is not to address issues associated with "unknown" missing data; rather, the focus centers on "known" missing data that become a hindrance to data processing. Consider this example: In an exam, item responses are stored in the format that each response of each respondent occupies a row (tall structure). When some subjects skip items, these item responses should be considered wrong answers. However, due to a design flaw, these rows are omitted rather than being stored as a blank response. Consider another example: In a study when the same subject re-takes the same exam, constant values of the same subject should be inherited from the previous records. However, for some reason, this inheritance function was not turned on in the database. As a remedy, this presentation demonstrates two SAS programs for auto-filling in missing data based on known values. | missing value, repeated measures, proc sort, proc step | source | 5 |
| 28 | Exploring SAS Generation Data Sets | Kirk Paul Lafler | Users have at their disposal a unique and powerful feature for retaining his torical copies of SAS data sets . Th is collection of copied generation data sets, representing versions of the same data set consists of a root member name and a unique version number , is aptly referred to as a generation group. This p resentation explores the power associated with generation dat a sets by showing users their purpose, h ow they are created and maintained, the approach used for accessing a specific version of a generation group , as well as other usefu l techniques. Simple coding examples will be illustrated to reinforce concept and application. Page 4 Copyright 2004-2005 by Software Intelligence Corporation Page 5 Copyright 2004-2005 by Software Intelligence Corporation | proc step, proc print | source | 10 |
| 29 | Find the Best Prospects for a New Product by Using a Data Mining Model | Ting Wu | The market department of a financial firm keeps records on customers, including demographic information and their type of accounts. The firm is launching a new product and wishes to determine who are the best prospects among the existing customers for this new product. You are asked to provide a list of 1000 best prospects. Regression, decision tree and neural network models are built to use for scoring the prospective customers, A confusion matrix is then used to determine what the cut-off point for the scores for customers you should use to determine who qualifies as a good target. The models are compared by assessing model performance and validating the model to new data. | proc hpsplit, neural network, regression analysis, data mining, training set, confusion matrix, predictor variable, network optimization, response variable, logistic distribution, logistic regression, data cleaning | source | 8 |
| 30 | Font Color or Highlight? You Name It! | Guanjun Feng | This paper introduces a method of combining PROC FORMAT, PROC REPORT and ODS (Output Delivery System) to modify the default output and export the output from SAS to MS EXCEL. This enables the user to easily highlight or change the font color of selected output, such as p-values that are smaller than 0.05. . |
proc step, proc format, proc report, estimate, excel, proc print, anova | source | 4 |
| 31 | From the Outhouse to the Penthouse: 5 Secrets to Enhancing Your Net Worth in the SAS Marketplace | Molly Hall | Isn't it odd that some of the most literate and educated people you've ever met, even people that seem so astute that they can't help but make buckets of money, often struggle to make ends meet? And on the other hand, I'll bet that you've met people who have hardly any education and might not seem very bright - yet who seem to make money as easily as flipping a light switch. One man is living in the penthouse, and the other is barely above living in an outhouse. Because I am a recruiter, I probably see this oddity in life more often than most. This paper will attempt to tackle some of the common (mis)perceptions regarding the SAS job market. What can you do to enhance your net worth? I will divulge some trade secrets with you. I will also discuss proactive ways to move your career in the right direction, as well as the advantages and disadvantages of contract and consulting work. | sas programmer, job security | source | 5 |
| 32 | Get the Correct Hazard Ratio from SAS PROC PHREG Procedure | Betty Ying Wang | Cox proportional hazards model is a commonly used model in providing hazard ratio to compare survival times of two population groups. The exponentiated linear regression part of the model describes the effects of explanatory variables on hazard ratio. PROC PHREG is a SAS procedure that implements the Cox model and provides the hazard ratio estimate. The estimate is interpreted as the percent change in the hazards of the two population groups given an increase of one unit in a given explanatory variable and conditional on fixed values of all other explanatory variables. When the increase in the given explanatory variable is not equal to one unit, the hazard ratio estimate should be interpreted with caution. This paper illustrates this issue via an example with a multi-level treatment group variable as an explanatory variable. |
hazard ratio, predictor variable, proc step, proc phreg, sas output, maximum likelihood estimation, regression analysis, likelihood function, nominal variable, class variable, hazard function, proc reg, survival analysis, survival function | source | 7 |
| 33 | Getting in to the Picture (Format) | Andrew H. Karp | Although part of the SAS Format facility for well over two decades, PICTURE Formats are often not well understood, and consequently tend to be underutilized by even experienced SAS users. Yet, they provide a wealth of tools to effectively portray the values of numeric variables and often avoid the need for either tedious DATA STEP coding or to create new variables in your data sets. This tutorial describes how PICTURE Formats "work," how to write them in PROC FORMAT statements and how to apply them in both Data and Procedure Steps. By mastering the concepts and techniques shown in this paper you will be able to apply the power of PICTURE formats in your SAS programs and in so doing often complete your data management, reporting and analysis tasks faster and easier than resorting to other potentially more tedious and time-intensive methods in the SAS System. |
proc step, proc format, numeric variable, proc print, credit card, proc report, datetime variable, format statement, data step, excel | source | 16 |
| 34 | Getting Your Random Sample In PROC SQL | Richard Severino | PROC SQL can be used to get a random sa mple from a large dataset with rela tive ease. A more common method of getting a random sample from a large dataset requires using the DATA STEP along with some programming or using the SURVEYSELECT procedure which became available in SAS/STAT beginning with SAS Version 8 . It is relatively easy to get a simple random sample using only the SQL procedure. |
sample, proc sql, proc step, where statement, order by, standard deviation, sample size, random sampling, proc freq, create table, data step | source | 14 |
| 35 | Guided Tour Through the SAS Windowing Environment | Casey Cantrell | The SAS system running in the Microsoft Windows environment contains a multitude of tools that can make the process of learning, and using SAS much easier. The interface simplifies communication with the operating system and provides us immediate and familiar access to our data. The programmer may perform common data management tasks with the click of a mouse, customize the working environment and use a variety of tools common to other Windows applications. This workshop will provide an orientation to the SAS windowing environment, and will demonstrate how to edit and submit programs, manage output, and build and save SAS system files. | sas output, proc step, sas library, work library, proc freq, sas log | source | 19 |
| 36 | Have It Both Ways: Macros that Produce Publication-Quality Tables and Stand-alone Code | Linda Collins, Lisa Kaye Brooks, Michael Rea, Alan Hopkins |
There's an intrinsic tension between setting up an elegant, modular programming system and writing simple, easy-to- understand programs. Macro libraries have enormous value as a production tool; they allow common applications to be written, validated, and reused easily. However, they often contain a great deal of obscure code designed to produce 'pretty' results. For some audiences - such as a statistician or regulatory reviewer who wants to verify that the desired procedures were coded correctly - a simple, straightforward program is preferable. The ideal program for this audience contains only the procedures necessary to produce statistical results (see CDISC Analysis Data Model Version 2.0). In this paper, we present a technique for satisfying both needs. The framework is an application macro library that runs common SAS statistical procedures and produces a finished report. | data cleaning, proc step, clinical trial, macro call, proc glm, proc genmod, data science, change from baseline, title statement, adam dataset, data governance, independent qc, clinical data, macro variable, macro definition, cdisc | source | 19 |
| 37 | Hierarchical Logistic Regression Modeling with SAS GLIMMIX | Jian Dai, Zhongmin Li, David Rocke |
Data often have hierarchical or clustered structures, such as patients clustered within hospitals or students nested within schools. Hierarchical models are statistical models that are used to analyze hierarchical or multilevel data. SAS GLIMMIX procedure is a new and highly useful tool for hierarchical modeling with discrete responses. This paper is focused on hierarchical logistic regression modeling with GLIMMIX. We present several applications of these models and show how to use GLIMMIX to fit the models and test hypotheses. We illustrate the applications using a sample data from a multi-institution database on coronary artery bypass grafting surgeries developed by the California Office of Statewide Health Planning and Development. |
fixed effects, logistic regression, random intercept, proc mixed, logistic distribution, regression analysis, sample, interaction effect, insurance risk score, odds ratio, response variable, proc glimmix, p-value, binary variable | source | 9 |
| 38 | How to Analyze Terabytes of Data with a Word | Mark Gustafson | Regardless of the industry in which one works, it is common to receive a large number of files with numerous variables and little documentation. This problem is compounded by the increasing size of files due to the decrease in the price of data storage in recent years. This paper will introduce a macro that will summarize all of the datasets in a library and then produce summary output for each variable. Several useful tricks with more general applications are also introduced. | proc step, proc freq, sas output, macro variable, proc contents, proc univariate, character variable, macro definition | source | 8 |
| 39 | How to Find Asnwers: Using SAS Resources | Ginger Carey, Helen D. Carey |
The vast amount of information on the SAS System, particularly that available through the Web, can be overwhelming. Knowing where to find the information you need saves time and frustration. This paper is about the variety of information and media available and describes where to learn more about SAS software and find answers to your SAS questions. | sample, sas base | source | 17 |
| 40 | How to Leverage Oracle Bulk-load for Performance | Jun Cai | Often times, we need to load a large volume of SAS data into an Oracle database. Bulk-load through th e SAS/ACCESS interface would be the first choice in many cases, but with a cost of dumping SAS data to a text file to accommodate SQL*Loader. SAS V9 further boosts th e bulk load by leveraging native Oracle features on load. However, it is crucial to understand how and when t o use those functions in order to optimize load for performance. Inappropriate uses of those options may not produce a desired result. Instead, it might jeopardize you r load. This paper is intended to explore some of these new feat ures and to discuss the various situations that sho uld be taken into consideration when applying those options in O racle bulk load. Sample code will be presented to e laborate the benefits gained by appropriate uses of those option s, including OR_PARTITION, BL_INDEX_OPTIONS, BL_RECOVERABLE, BL_SUPPRESS_NULLIF and BL_OPTIONS. |
dataset, raw data, sample, oracle, key, sas programmer, txt, proc append, proc step, proc sql | source | 10 |
| 41 | Information for All: Why and How to Make Accessible SAS Output | Jennifer S. Harper, Lisa Pappas |
We will review the social and legal reasons for making your online information accessible to people with disabilities as well as techniques to make it happen. Government and educational institutions have legal mandates to make web sites accessible to users with disabilities, but what organization wouldn't benefit from presenting organized, usable, and accessible information? In fact, recent legal cases have applied the Americans with Disabilities Act to private industry web sites. This talk will introduce the web standards and government regulations related to accessibility. We will then focus on the accessibility standards most relevant to SAS ODS graphs and tables. Besides demonstrating techniques for creating accessible SAS tables and graphs, we will show free online tools for testing the accessibility of your SAS output. |
proc step, proc report, ods html, sas output, proc tabulate, ods pdf | source | 7 |
| 42 | Introduction to Formats: Simplifying Programming and Improving Data Presentation | Elizabeth A. Roth | SAS' format facility is at the same time a simple tool for presenting data and a powerful tool for manipulating it. This paper provides a general overview for the beginning SAS programmer, and then presents several ways formats can solve common but diverse SAS programming challenges in presenting output, reading in data, creating new variables, and filtering observations. I provide a 'toolbox' of useful and elegant solutions using formats that are easy to learn and begin using right away. This paper also presents format solutions to more complex data matters that advanced programmers can appreciate as well. | proc format, proc step, raw data, format statement, char., proc freq, sysdate9, input statement, user-defined format | source | 11 |
| 43 | Introduction to Summary Tables in SAS Enterprise Guide | Lora D. Delwiche, Susan J. Slaughter |
This paper shows, step-by-step, how to use the Summary Tables task in SAS Enterprise Guide to produce polished reports. Summary Tables is based on the TABLULATE procedure, but you don't need to know PROC TABULATE or even how to write SAS programs to use this feature. Whether you are a seasoned SAS programmer or new to SAS, |
sas enterprise guide, proc tabulate, proc step | source | 15 |
| 44 | Just Skip It | Helen D. Carey | When developing SAS programs, we sometimes (or often) want to either submit only a portion of the code or skip some of the code. This paper explains how to do both and offers tips and techniques to make the coding easier to do and easier to identify. | comment statement, comment, macro variable, macro definition, proc step | source | 8 |
| 45 | Make Your IT Resource Work Hard When and Where You Need In Your BI Environment | Barbara Sutton/Sue Davis | One common problem in data centers is you have a group of system reach almost 100% of utiliza tion while some systems right next to them are only used at 15-20%. While you have 80-85% computing resource idle, you also have applications on those over utilized system suffering which could seriously impact your business. The HP Virtual Server Environment (VSE) for HP Integrity and HP 9000 servers help customers achieve a greater return on IT investments by optimizing server resource utilization in real time based on business priorities. VSE creates virtual servers that can automatically grow and shrink based on the service-level objectives (SLOs) set for each application they host. Through tight integration with partitioning, high availability and utility pricing, VSE allows customers to maintain service levels and to pay for spare capacity only as -needed. | business intelligence, oracle, sas enterprise guide | source | 10 |
| 46 | Mapping Performance Skills: A Leadership Code Book | MaryAnne DePesquo Hope, Laura A. Giordano |
A leader should assess each team member based on techni cal skills and defined performanc e characteristics. It is important to match the individual to the task and be pr epared to adapt when circumstances mandate. The evaluation process becomes a daily task for a manager as project prio rities shift. This paper will explore ideas and techniques that can assist a leader in evaluating performance and optimizing resources available. | source | 2 | |
| 47 | Missing Fun with Functions | Kathryn A. Valdes | Whatever type of data you collect, analyze, report on, explore, or play with, missing values could be lurking within. It would be great to assume that every row and column in a tabl e is populated, that for every variable there is a value. This is definitely not a safe assumption. It may be necessary-and probably good practice in general-to account for missing values in a program. Functions to the rescue. There are several functions that can programmatically find missing values. Each of those functions approaches the problem in a different way. Wh y would you choose one function over the other? Are there advantages or disadvantages in using these functions? The answer is: it depends. The fun-and challenge-of using SAS is that there are so many different ways to accomplish a single task; and sometimes it is just fun to code things in different ways just because you can. | missing value, sum, missing function, proc sql, proc step | source | 5 |
| 48 | Mixed Model Selection via the Information-Theoretic Approch using SAS | Jesse Albert Canchola, Torsten B. Neilands |
Model-building via the hypothesis-testing method (i.e., variable se lection) has been the de-facto standard in the statistical analyst's toolbox for some time. Other methods that include Bayesian, cross-validation and the bootstrap are excellent competing methods but can be complex and resource intensive. Still, superior predictive algorithms such as neural networks, random forests or support vector machines can be used but suffer from interpretability limitations (Breiman, 2001). The information-theoretic approach to model selection, as codified by Burnham and Anderson (2002), involves averaging over a class of eligible models to provide robust inference. Th e IT approach side-steps the (sometimes controversial) issue of judging whether individual variables are important or not based on the traditional hypothesis testing dichotomy. We utilize the SAS COMPMIX macro to carry out the IT paradigm. We illustrate with an example from AIDS research. | feature selection, macro definition, hypothesis testing, likelihood function, sample, proc mixed | source | 7 |
| 49 | Model Selection Methods in PROC MIXED | George C. J. Fernandez | A user-friendly SAS macro application to perform all possible model selection of fixed effects including quadratic and cross products in the presence of random and repeated measures effects using SAS PROC MIXED is available. This macro application will complement the model selection option currently available in the SAS PROC REG for multiple linear regressions and the experimental SAS PROCEDURE GLMSELECT that focuses on the standard independently and identically distributed general linear model for univariate responses. Options are also included in this macro to select the best covariance structure associated with the user-specified fully saturated repeated measures model; to graphically explore and to detect statistical significance of user specified linear, quadratic, interaction terms for fixed effects; and to diagnose multicollinearity, via the VIF statistic for each continuous predictors involved in each model selection step. |
feature selection, fixed effects, covariance, proc mixed, repeated measures, proc step, covariance structure, restricted maximum likelihood, predictor variable, macro definition, correlation, data visualization, spearman correlation, sample, interaction effect, sample size, akaike information criterion, dataset, excel | source | 19 |
| 50 | Multi-Dimensional Data Presentation | Karl Glaser | Human beings cannot see associations in data beyond two or three dimensions. In this paper, simple SAS tools will be used to display relationships in data in three, four, and five dimensions, with some interesting and perhaps surprising results. | outlier, proc gplot | source | 8 |
| 51 | n the Compute Block: Issues Associated with Using and Naming Variables | Arthur L. Carpenter | In the compute block of the PROC REPORT step variables can be both created and used. They may be on the incoming data table, they may be computed in the REPORT step, they can be statistical summaries, and they may even be artificially created by the REPORT process. In the DATA STEP each variable has a name and that variable's name is used whenever we want to address a particular value on the Program Data Vector. In the REPORT step things are not so simple. The origins and use of a variable will determine how it is to be named in a compute block. If you do not know how to properly address a variable or report item in the compute block, the compute block will often fail. This paper will discuss the various variable types, how they are used, and how they are addressed in the compute block. |
proc report, proc step | source | 9 |
| 52 | New CDISC Training Initiative | Dana Rafiee | The U.S. Food and Drug Administration's recommendations in July 2004 and March 2005 that study data submissions use CDISC standards created an industry need for training in CDISC standards. Every company expecting to make a submission within the next three years should understand the role of CDISC standards in submissions and in study database design and many, if not most, will look for training. In January 2006, CDISC, Destiny Corporation and Zurich Biostatistics, Inc. (ZBI) signed an agreement to jointly develop flexible, web or CD-ROM based, on-demand training that will be distributed by CDISC itself, as well as by the other two partners. | crf, adam dataset, cdisc | source | 2 |
| 53 | Optimizing Your SAS Performance with Grid Computing | Sy J. Truong | Google has become very successful by developing an efficient search engine running on commodity hardware. It no longer uses the old model of putting all its resource onto one super computer, but rather it spreads that processing onto a cluster of smaller machines running in parallel to form a grid. Gordon Moore made an observation in 1965 predicting that the number of transistors per square inch used on computers would double every year. This trend has become law and continues to elevate the ubiquitous and relatively inexpensive desktop and laptop computers. This paper will discuss how you can cluster computers in a grid to optimize the execution of SAS programs. | grid computing, credit card | source | 10 |
| 54 | Output Delivery System Tips and Techniques | Kirk Paul Lafler | Are you looking for ways to improve the way your output appears? The SAS Output Delivery System (ODS) can help turn tired-looking output into great looking information with a purpose. ODS introduces exciting new features for your output. Gone are the days when the only available formatting choice is basic output listings consisting of monospace fonts printed on a white background. Using built-in format engines, ODS addresses the inherent weaknesses found in traditional SAS output by enabling "quality" output to be produced without having to import it into word processors. In Version 9, many new output formatting features and options are introd uced for SAS users to take advantage of. Users have a powerful and easy way to create and access formatted procedure and DATA STEP output. |
proc univariate, output delivery system, sas output, proc format, ods html, ods pdf, ods rtf, dataset, proc step | source | 12 |
| 55 | Parameter Gathering Techniques for Data Driven Programming | Renato G. Villacorte | Data Driven Programming maximizes procedural efficiency and lightens the workload of routine processing. This paper presents techniques for gathering the parameters such as filenames and procedure results that can be used in subsequent routines and macro programming. Techniques include utilizing the SYSEXEC, OUTPUT=, and ODS OUTPUT statements. Examples of these coding techniques include how to read an MS Windows folder's filenames into a SAS dataset, how to extract PROC results into datasets, and how to make SAS write programs based on these |
macro variable, dataset, proc step, output, logistic distribution, macro definition, proc contents | source | 5 |
| 56 | PDF Can be Pretty Darn Fancy: Tips and Tricks for the ODS PDF Destination | Peter J. Lund | We're not too far removed from the days when presentation-ready SAS output meant lots of cutting and pasting or retyping. Now, the ODS PDF destination enables you to produce high quality output the first time, without other tools or applications. This paper explores a number of ODS options in general and, more specifically, their use in creating PDF output. We will cover ODS ESCAPECHAR, which allows for inline formatting of titles, footnotes and other text; ODS LAYOUT, which lets you place output wherever you want it on the page - even output from more than one procedure; inline formatting in PROC REPORT; overlaying output from SAS/GRAPH procedures on top of other output and more. We'll work from real life examples and see how you can produce output that looks like it took hours to create. |
proc step, proc report, ods pdf, footnote statement, output delivery system, proc sql | source | 19 |
| 57 | PROC GENMOD with GEE to Analyze Correlated Outcomes Data Using SAS | Tyler C. Smith, Besa Smith |
Generalized linear models provide a framework for relating response and predictor variables by extending traditional linear model theory to nonlinear data. This is very important in many areas of epidemiologic research where outcomes are dichotomous or otherwise not normally distributed. To potentially complicate the statistical approach further, some studies, such as prospective cohort studies, follow individuals for a sufficient duration of time to observe multiple disease occurrences. If used as response variables, these disease outcomes are often correlated and should be treated with statistical consideration encompassing repeated measures applications in the regression analyses. Generalized Estimating Equations (GEEs) offer a way to analyze such data with reasonable statistical efficiency. | proc step, regression analysis, correlation, logistic distribution, proc genmod, logistic regression, proc logistic, odds ratio, estimate, likelihood function, response variable, confidence interval, proc calis, covariance, binomial distribution, predictor variable, proc reg, proc mixed, sample, covariance structure, maximum likelihood estimation, repeated measures, poisson distribution, proc glm, normal distribution | source | 14 |
| 58 | PROC SQL: A Powerful Tool to Improve Your Data Quality | Keh-Dong Shiang | The Structured Query Language (SQL) is a standardized query language that can be efficiently used to retrieve and update data stored in relational databases. However, SQL's WHERE-clause row-selection feature can be interestingly utilized for data validation and error checks. In this paper, I will present a method of designing a highly structured validation program, which is applied to implement a data quality assurance procedure for collecting clean clinical data in the Diabetes department at City of Hope National Medical Center. With both SAS macros and SQL procedures used, the results will demonstrate the following benefits: easy database maintenance with no hard-coded checks, transparency of sophisticated error-check edits to the users, effectively-geared with nested structures, easy- to-understand reporting system, and considerable reduction of the total programming time required. | data cleaning, proc sql, data validation rules, where statement, proc step, sample, error message, quality assurance, excel, missing value, adverse events, clinical data, dataset, numeric variable | source | 10 |
| 59 | Real-Time Sales Volumes Projection with SAS - A Challenge in Implementing a Data-Based Solution in the Automobile Industry | Keiko I. Powers | One of our major data mining projects focuses on projecting automobile sales volumes in the United States using the point-of-sales data transmitted and archived daily by a large sample of automobile dealers. Since the data by these dealers are sample data, analysis steps for estimating the sales volumes in US require applications of complex sampling and weighting techniques. Because the data are 'noisy', which is typical for scanner-type data, and the volume of data to be processed is very large, estimating the sales volume accurately is a challenging project goal. In addition, the need to provide the sales volume estimates in a very short period of time on a regular basis is another factor which makes the project very challenging. I will discuss how the project team has coped with these challenging issues and implemented sales projection methodology successfully with SAS. | confidence interval, sample, time series analysis, sample size, transaction data, poisson distribution, data wrangling, proc freq, binomial distribution | source | 8 |
| 60 | Release 2.1 of SAS Add-In for Microsoft Office - Bringing Microsoft PowerPoint into the Mix | Jennifer Bradshaw Clegg, Eric Hill |
Release 2.1 of SAS Add-In for Microsoft Office builds on t he functionality introduced in release 1.3 of SAS Add-In for Microsoft Office. With release 2.1, Microsoft PowerPoint users can access the power of SAS and create presentations with refreshable SAS content. SAS data acce ss support in Microsoft EXCEL is greatly expanded in release 2.1; users can open SAS data sources of any size, eit her directly into worksheets or into Microsoft EXCEL PivotTables, and users can easily create a SAS data set from Microsoft EXCEL data. For reporting, release 2.1 facilitates formatting SAS reports using the Microsoft Office application, and intermixing SAS content with other content in Microsoft EXCEL. |
excel, dataset, sas enterprise guide, xls, sas programming, proc sql | source | 20 |
| 61 | Sample Survey Data and the Procs you OUGHT To Be Using | David L. Cassell | Statisticians and data analysts frequently have data sets which are difficult to analyze, with characteristics that break the underlying assumptions of the simpler analytical tools. But SAS has tools to handle more complex problems. And, now that SAS has survey analysis procedures, there's no longer a need to pretend that survey samples should be analyzed using non-survey design tools. Now that large databases are commonly sampled for marketing, data mining, and scientific research, the need to use survey analysis procedures is increasing. In this paper, we'll look at several common situations and illustrate how the data are usually analyzed. Then we will show how the data ought to be analyzed using the SAS survey analysis procedures, and what the consequences of choosing the wrong procedures may be. | sample, proc step, sample weight, proc calis, proc surveyreg, proc glm, proc freq, regression analysis, chi-square test, confidence interval, proc reg, proc surveyfreq, proc surveylogistic, population, proc genmod, random sampling, proc surveymeans, logistic distribution, proc means, degrees of freedom, proc logistic, anova, estimate, survey weight, logistic regression, covariance, data science, proc sql | source | 13 |
| 62 | SAS Date and Time Functions 101 | Nikki M. Carroll | Since the beginning of time, people have had the desire to document the passage of hours, days, weeks and years. After documenting the passage of time, calculating differences in time came next. Calculating this passage of time using dates, times and datetimes in SAS can seem intimidating at first, however, after learning a few tips and tricks, this task can be invigorating and, some might say, fun! An introduction to SAS dates will be discussed as well as techniques for 1) formatting dates, 2) reading dates into datasets, 3) how to manipulate data using dates, and 4) using functions with dates. | sysdate9, date variable, format statement, numeric variable, time, mdy, dataset, proc step, datepart, proc print, datetime, options statement, yrdif, input statement, put statement | source | 10 |
| 63 | SAS Macros for Creating Custom Formatted Excel Workbooks | James J. Van Campen | This paper presents a system of SAS macros for creating custom formatted EXCEL workbooks. Detailed control of workbook formatting is attained directly from SAS by using the Microsoft XML Spreadsheet Schema (XMLSS). The macros first write an XML file conforming to XMLSS, then use DDE to open the XML file with EXCEL and save it as an EXCEL workbook. SAS 9.1 and EXCEL 2002 under the Windows XP operating system were used for the examples. Some familiarity with the SAS macros, DATA _NULL_ reporting, and markup languages is helpful but not necessary. |
excel, xls, macro call, macro definition, txt, %if-%then-%else, macro quoting function | source | 6 |
| 64 | SAS's Various Varying Variables, or 1000+ Ways to Manipulate SAS Variables | Paul A. Choate | This presentation is an overview of SAS data set variables in two parts: PART 1: VARIABLE ATTRIBUTES: Definition of Variable Attributes and the PDV ; Changing Variable Attributes with DATA STEP Statements ; Indexing Variables, Changing Variable Types, and Reordering the PDV ; Changing Variable Attributes with Procedures ; Variable Information Functions and Call Routines. PART 2: VARIABLE CONTENTS AND HANDLING: Tokens, Name Literals, and Special Constants ; Number of Variables and Variable Size Limits ; Variable Lists in Functions, Statements, Options, and Arrays ; Variable Comparisons. |
format statement, data step, informat statement, attrib statement, label statement, numeric variable, name range list, proc sql, length statement | source | 14 |
| 65 | SAS/AF Software Rises Again - Enhancements in SAS 9.2 | Lynn Curley | Is there a future for SAS/AF software? Absolutely! We hav e heard from many customers that SAS/AF is particularly useful for the development of interactive applications that can be executed with as little as an installation of Base SAS. In response to this feedback, SAS wants to demons trate its support for SAS/AF, ensuring that SAS/AF will continue to be a useful product for many years to co me. Accordingly, SAS/AF and the VIEWTABLE window are being enhanced to include support of long format names. Two cl asses with experimental status, the Dual Selector Control and the Tree View Control, are being enhanced and pr omoted to production status. This paper discusses the exciting enhancements to the classes and demonstrates how to use them. |
sas base, options statement | source | 14 |
| 66 | SAS/MACRO NOTES: Lines and Columns in the Log | Paul D. Sherman | Do you shy away from macros for lack of their source line number error reporting? Are those bogus (Line):(Column) values driving you nuts? If so, this article is for you. The SAS Macro Language is a good thing. However, locations of errors from code placed inside macros are reported in a slightly different way than from traditional open code. We show how to understand both macro and open code, and present a few clear and simple examples with some techniques for quickly debugging SAS Macros. Skill Level: Intermediate to Advanced Base/SAS users, especially those timid of macros. | macro definition, macro put, data step, call execute, cpu time, &variable, comment, macro variable | source | 11 |
| 67 | Simple Rules to Know When Working With Indexes | Kirk Paul Lafler | SAS users are always interested in learning techniques related to improving data access. One way of improving information retrieval from a SAS data set or table is to create and use an index. An index consists of one or more columns that are used to uniquely identify each row within a table. Functioning as a SAS object containing the values in one or more columns in a table, an index may be defined as numeric, character, or a combination of both. This presentation will emphasize the rules that users should know when creating and using indexes to make information retrieval more efficient. | where statement, proc sql, index, proc step | source | 4 |
| 68 | Skewness, Multicollinearity, Heteroskedasticity - You Name It, Cost Data Have It! Solutions to Violations of Assumptions of Ordinary Least Squares Regression Model Using SAS | Leonor Ayyangar | What options are available to the researcher when one or more assumptions of an ordinary least squares (OLS) regression model is violated? This poster highlights SAS procedures that may be employed when data at hand does not fulfill the assumptions underlying OLS. | proc step, regression analysis, predictor variable, proc reg, sample, response variable, residual, proc calis, correlation, outlier, null hypothesis, proc autoreg, proc robustreg, instrumental variables | source | 7 |
| 69 | Step by Step PROC CDISC and More | Sy J. Truong | There are many crucial steps that are required during the implementation of the CDISC data models. This paper presents a step by step procedure that is based on real world implementations. Rather than stumbling through trial and error, these steps are based on lessons learned from implementations of CDISC SDTM version 3.1. This paper will cover technical challenges along with methodologies and processes. Some of the topics covered include: Project Definition, Plan and Management Data Standard Analysis and Review Data Transformation Specification and Definition Performing Data Transformation to Standards Review and Validation of Transformations and Standards Domain Documentation for DEFINE.PDF and DEFINE.XML The regulatory requirements are going to include CDISC in the near future and the benefits are obvious. It is therefore wise and prudent to implement with techniques and processes refined from lessons learned from real life implementations. | proc step, dataset, excel, data cleaning, proc contents, adverse events, length statement, cdisc | source | 19 |
| 70 | Steps to Success with PROC MEANS | Andrew H. Karp | This paper discusses in detail the steps needed to successfully use PROC MEANS: the analysis variables to summarize, optional classification (or BY) variables, and the statistics requested. The discussion covers the statistics keywords, placement of the statements in the procedure step, and how results are stored in output data sets. |
proc step, proc means, output, sas output, missing value, dataset, by group processing, numeric variable, where statement, character variable, credit card, data science | source | 12 |
| 71 | Techniques For Introducing SAS To Novice Programmers | Renato G. Villacorte | Most often, entry-level statistical programmers have some experience in procedural programming or menu-driven statistical applications. To make these users instantly productive with SAS, one may only need to provide them with exemplars of code and proper syntax. This paper presents several useful techniques for providing useable syntax to novice programmers using the features in Enhanced Editor and methods of Data Driven Programming. Keyboard macros and data entry abbreviations are helpful methods of providing proper syntax for commonly used procedures and code blocks. Data Driven Programming (the use of macros that create programs based on the data provided by other programs) is extremely useful in helping novices contribute quickly to the work flow. These techniques, accompanied with a comprehensive training curriculum, greatly reduce the time it takes for novices to gain confidence and expertise using SAS software. | sample, proc freq, put statement, proc step | source | 4 |
| 72 | Testing for Verb Types in Spanish: An Application of SAS to Corpus Linguistics | Roberto Mayoral Hernández, Lindsey N. Chen |
The last decade has seen an increase in the use of technologies in linguistics research. The availabi lity of online corpora and powerful statistical software to ols have made it possible to handle large amounts o f textual information and crucially, to shed some lig ht on certain linguistic theories. Within this pap er, SAS was utilized to study the relation between verb typ es and subject position in Spanish. Applying SAS procedures to data obtained from CREA (Corpus de Referencia del Espanol Actual), we show that transitive, unergative and unaccusative verbs can be successful ly differentiated by looking at the position of the ir subject. Furthermore, we conclude from our analysis that verbs of light and sound emission (VLSEs) sho uld be, contra Mendikoetxea (1999), classified as unacc usative verbs rather than unergative verbs. | proc freq, data science | source | 6 |
| 73 | Text Mining Internet Discussion Groups: an Automotive Case Study | John Wallace | Public domain information can provide a unique source fo r data collection for both academic and corporate research. This paper explains how systematically analyzing the USENET can benefit companies, what the modeling process entailed and the steps required to clean the data. The USENET data serves as the platform to discuss SAS Text Miner and the process involved in using it and other analytical tools for text mining. The pre- processing of data, exploratory analysis and development of synonym and stop lists is covered. The final model is a hierarchy of Expectation Maximization Clustering models that total over 100 clusters. | score code, proc step, excel, data mining, macro definition | source | 7 |
| 74 | The Basics of Structural Equation Modeling | Diana D. Suhr | Structural equation modeling (SEM) is a methodology for representing, estimating, and testing a network of relationships between variables (measured variables and latent constructs). This tutorial provides an introduction to SEM including comparisons between "traditional statistical" and SEM analyses. Examples include path analysis/ regression, repeated measures analysis/latent growth curve modeling, and confirmatory factor analysis. Participants will learn basic skills to analyze data with structural equation modeling. Rationale Analyzing research data and interpreting results can be complex and confusing. Traditional stat istical approaches to data analy sis specify default models, assume measurement occurs without erro r, and are somewhat inflexible. However, structural equation modeling requires specification of a model based on theory and research, is a mult ivariate technique incorporating measured variables and latent constructs, and explicit ly specifies measurement error. | estimate, proc step, latent variable, covariance, proc glm, repeated measures, regression analysis, proc calis, anova, correlation, proc genmod, sample, repeated measures anova, factor analysis, confirmatory factor analysis, residual, maximum likelihood estimation, proc reg, likelihood function, sample size, data science | source | 19 |
| 75 | The Beginners Guide to ODS Markup: Don't Panic! | Eric A. Gebhart | The MARKUP destination is the most powerful Output Delivery System (ODS) destination. The ODS MARKUP statement can create a multitude of output file types fr om Comma Separated Values (CSV) to Compact HyperText Markup Language (cHTML), HTML 4.0, Extensible HyperText Markup Language (XHTML), LaTeX, and an endless variety of Extensible Markup Language (XML) flavors. It can also create Microsoft EXCEL output. Learn how easy it is to use this destination, and learn how it works. Get the most out of this versatile ODS destination with minimum effort. |
excel, ods html, output delivery system, sas output, proc template, proc step | source | 20 |
| 76 | The Easy Way to Flexible Code: SAS Macro Variables | Mel Widawski | Expert programmers use existing code and modify it rather than programming from scratch. You can use macro variables to facilitate writing code that is flexible and easy to re-use. Put information such as file locations, lists of variables, and selection criteria in macro variables at the beginning of a program and simply refer to them as needed throughout the program. You will learn how to create and use these macro variables. You can use these macro variables without having to learn the SAS macro language. Using macro variables is extremely simple. | macro variable, macro put, &variable, macro facility | source | 8 |
| 77 | The Logic and Logistics of Logistic Regression | Lawrence Rasouliyan, Dave P. Miller |
Although logistic regression models are widely used in multivariable analyses with binary outcomes, many of their features, which can be very helpful tools in bette r understanding the data, ar e often underutilized. In addition to the widely used and reported odds ratios and p-values, PROC LOGISTIC generates a plethora of statistics from which one can gain further insight, ma ke stronger analytical inferences, and more easily identify errors in the model construction. Model options will be explored, and re sulting outputs from real- world examples will be explained in detail. Topics co vered include model fit statistics, maximum likelihood estimates, effect modification, Receiver Operator Characteristic (ROC) curves, and the Hosmer-Lemeshow Goodness of Fit Test. |
odds ratio, logistic distribution, logistic regression, proc logistic, regression analysis, proc step, main effect, predictor variable, response variable, binary variable, maximum likelihood estimation, confidence interval, goodness of fit, likelihood function, interval variable, roc curve, stepwise selection, nominal variable, class variable, interaction effect, missing value, feature selection, proc freq | source | 14 |
| 78 | Time to Take the Wheel: A Hands-On Introduction to SAS Programming | Casey Cantrell | This workshop is intended to give the new programmer hands-on experience working with SAS. Although we will use tools available in the SAS windowing environment, the workshop will address basics common to SAS running on all operating systems. Topics include how to read data into SAS, how to work with data in SAS, and how to extract information from a SAS system file. Where applicable, we will demonstrate both programming and graphical methods to accomplish these tasks. | proc step, by group processing, proc print, proc freq, proc univariate, proc contents, filename statement, infile statement, proc sort, sashelp | source | 20 |
| 79 | Tips & Techniques with PROC MEANS | Andrew H. Karp | proc step, proc means, dataset, output, class variable, numeric variable, by group processing, character variable, sas base, sas output | source | 8 | |
| 80 | Turn Your SAS Programs into Web Applications, the Simple Way | Mickey Cheng | CASPER (CDM's Advanced SAS Portal for Extraction and Reporting) is a web based application that provides a simple and effective way to build web fronts for SAS programs developed in the conventional SAS environment. The system employs CGI (Perl) technology to deliver web based functionality in a consistent fashion. The directory structure of the underlying operating system (Windows or UNIX) is built into the application. It also employs clickable icons for parameter files that store parameter values, which can be modified and saved. This paper is targeted at SAS programmers who are interested in expanding the usage of their SAS programs to non-SAS users, as well as managing SAS programs in an efficient and intuitive way. | sas programmer, advanced sas | source | 7 |
| 81 | User-Written DATA Step Functions | Jason Secosky | For years, users have requested the ability to write functions in DATA STEP syntax. The day has come when this is possible. In SAS9, PROC FCMP, provides the ability to write functions using DATA STEP syntax. In SAS 9.2, these functions can be called from DATA STEP. This paper will introduce how to write functions with PROC FCMP, how these functions are invoked from a DATA STEP, and caveats to be aware of when calling PROC FCMP functions from DATA STEP. |
proc step, proc fcmp, return, put statement, file statement | source | 9 |
| 82 | Using Arrays to Calculate Medication Utilization | Scott P. Leslie | Assessing duration of medication therapy involves managing a data set with multiple prescriptions per patient. This paper offers an innovative approach to calculating medication utilization as the proportion of days supplied over a specified time period. In this example, a few data steps use simple arrays to create multiple indicator variables which are then used to calculate medication utilization. Variations of this code can calculate gaps and overlaps in therapy as well as calculate concomitant medication utilization. | do loop, binary variable | source | 4 |
| 83 | Using Propensity Scores To Adjust For Treatment Selection Bias | Scott P. Leslie, Patrick Thiebaud |
Estimating the effect of drug treatment on outcomes requires adjusting for many observed factors, particularly those influencing drug selection. This paper demonstrates the use of PROC LOGISTIC in creating propensity scores to address such potential treatment selection bias. In this example using a pharmacy claims database, this method evaluates the probability of patients being included in five treatment groups and calculates weights based on factors hypothesized to influence treatment selection. A propensity score-weighted regression model is then fitted to compare the outcome of adherence between groups and to study the possible predictors of adherence. |
propensity score, regression analysis, logistic distribution | source | 4 |
| 84 | Using SAS Catalog and Printto to Document SAS Projects | Patrick Thornton | SAS CATALOG and PRINTTO procedures provide a convenient way to document SAS projects. Using SAS 9.1 on a Windows XP operating system, this paper demonstrates the use of PRINTTO to save and document SAS syntax, OUTPUT, and LOG as entries to a SAS catalog. Examples for the beginning SAS user show how a description of up to 256 characters may be associated with each entry, how entries may be viewed through the SAS Explorer window, and how all catalog metadata may be displayed. Macro programs are also included that may be used with existing programs to save a version of the OUTPUT, LOG and syntax to a catalog. | proc printto, catalog, catalog entry, sas syntax, proc catalog | source | 5 |
| 85 | Using SAS Macros to Track Student Retention, Progression, and Graduation Rates | Gary Levy, David Ma |
Accurately tracking students as they progress through college is a traditional and significant metric used in higher education assessment. For example, reporting number of applicants, student enrollment, retention (enrollment from one term to another), progression (progressing through academic levels), and graduation rates are all required elements provided annually to the Department of Education (i.e., IPED's) as well as many surveys of higher education (e.g., US News & World Report; Princeton Review, etc.). Moreover, enrollment management activities within US colleges and universities are constantly looking for measures to facilitate strategic student recruitment and student success for varied student cohorts (e.g., athletes, ethnic minorities, specified majors/ programs). Use of SAS software, more specifically SAS macro's, makes achieving these varied aspects of student tracking easier, and performed more efficiently and flexibly. | macro definition, data step, dataset | source | 10 |
| 86 | Using SAS to Create Teradata Multiload Scripts and Check Load Status | Wei Zhang | Teradata Multiload scripts are used to load data into a Data Warehouse. The regular method of writing one Multiload script is: first, copy an existing Multiload script and rename it; second, make modifications such as changing database names, changing table names, changing attribute names, etc .. When there are many tables with a large number of attributes that need to be lo aded, the above method is time consuming, difficult to debug, and prone to errors. This paper explores a step by step methodol ogy of using SAS macros to automatic ally generate multiple load scripts quickly and accurately. These load scripts are saved automatical ly into different files following a set of local naming conventions. In addition, this paper describes a method to send out error table report by email after the load, allowing the load status to easily be checked. | macro definition, create table | source | 6 |
| 87 | Using SAS/CONNECT and the Windows API to Preserve File DateTime Stamps in a Platform Migration Project | Wayne F. Woo | In certain circumstances it is desirable to retain the file DATETIME stamp when moving or copying a file from one location to another. This is a trivial manner when performing such functions under one operating system because the COPY and/or MOVE commands typically do not alter the DATETIME stamp. But what about moving or copying files between different operating systems? The standard file transfer protocol (FTP) does not give an option to preserve DATETIME stamps, but many vendors have created FTP software with this feature. This short paper will describe how to achieve this outcome with SAS code. The code transfers files between the OpenVMS operating system and Windows using SAS/CONNECT. Windows API functions are then called to set a file's DATETIME stamp to match with VMS. This code was used as part of a platform migration project. With slight modifications, the code should work equally well for Unix-to- Windows file transfers. |
dataset, proc step, proc download | source | 5 |
| 88 | Wait Wait, Don't Tell Me… You're Using the Wrong Proc! | David L. Cassell | Statisticians and data analysts frequently have data sets which are difficult to analyze, with characteristics that break the underlying assumptions of the simpler analytical tools. But SAS has tools to handle more complex problems. And, now that SAS has survey analysis procedures, there's no longer a need to pretend that survey samples should be analyzed using non-survey design tools. Now that large databases are commonly sampled for marketing, data mining, and scientific research, the need to use survey analysis procedures is increasing. In this paper, we'll look at several common situations and illustrate how the data are usually analyzed. Then we will show how the data ought to be analyzed using the SAS survey analysis procedures, and what the consequences of choosing the wrong procedures may be. | proc step, sample, proc calis, sample weight, proc surveyreg, regression analysis, confidence interval, proc glm, chi-square test, population, proc surveyfreq, proc surveylogistic, proc mixed, proc genmod, proc reg, logistic regression, proc surveymeans, degrees of freedom, logistic distribution, anova, estimate, random sampling, survey weight, proc means, cluster sampling, data science | source | 11 |
| 89 | What's New in Data Quality? | Pat M. Herbert | Data quality is becoming recognized as one of the most important areas associated with the success or failure of large scale Data Integration and Business Intelligence deployments. In order to make sense of data and to then tie related information together, you need to make sure your data is clean and consistent. You also need to provide accurate, reliable results to your information consumers in the form of reports. SAS and DataFlux have developed several new features and enhancements in order to improve the overall quality of your data which will put you on the path to successful Data Integration and Business Intelligence deployments. | data cleaning, web service, business intelligence, data validation rules, customer satisfaction | source | 6 |
| 90 | What's the DIF? | Nikki M. Carroll | There are numerous ways to calculate the differences between dates and, in particular, calculate ages. Some of the calculations include functions (DATDIF and YRDIF) and some calculations pound out age with formulas. But what's the difference between them? Is one better then the other? This paper will describe several different ways to calculate age and compare them so programmers can decide which formula is most accurate and/or easiest for them to use. | floor, yrdif, datepart, missing value | source | 4 |
| 91 | WHERE vs. IF Statements: Examples in Usage and Differences | Sunil Kumar Gupta | When programming in SAS, there is almost always more than one way to accomplish a task. Beginning programmers may think that there is no difference between using the WHERE statement and the IF statement to subset your data set. Knowledgeable programmers know that depending on the situation, sometimes one statement is more appropriate than the other. For example, if your subset condition includes automatic variables or new variables created within the DATA STEP, then you must use the IF statement instead of the WHERE statement. This paper shows you how and when to apply the WHERE and IF statements to get correct and reliable results. It also reviews the similarities as well as the differences between these two SAS programming approaches. |
if-then-else, where statement, error message, colon modifier, automatic variable, character variable, dataset | source | 11 |
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