MWSUG 2009 Papers - Index

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

#	Title ×	Authors ×	Abstract ×	Keywords ×	Pages
1	A Class of Predictive Models for Multi-Level Risks	Wensui Liu, Chuck Vu, Sandeep Kharidhi	In the financial service industry, discriminant analysis and its variants based upon binary out come, such as logistic regression or neural networks, are lar gely used to develop predictive models. Ho wever, the two-state assumption of such models over-simplifies customers' behavioral outcomes and ignores the exist ence of multi-level risk. In many situations, the financial impact of a certain customer is directly related to the frequency and the severity of his/her adverse behaviors. Therefore, it is of interest to model and predict such multi-level risks. Several modeling techniques, from Poisson to Ordered Logit models, have been widely discussed in numerous research literatures about how to predict the multi-level ri sks. Our paper is also an attempt cont ributed to this end. Several modeling strategies together with their SAS im plementations and related scoring scheme will be illustrated	poisson distribution, poisson regression, negative binomial regression, binomial distribution, regression analysis, logistic regression, logistic distribution, gain chart, likelihood function, probability functions, credit card, proc freq, proc step, proc sort, response variable	13
2	A Comparison of Decision Tree and Logistic Regression Model	Xianzhe Chen	This paper applies a decision tree model and logistic regression models to a real transportation problem, compares results of these two methods and presents model building procedures as well . The data set is partitioned into train, validation and test data. Due to the skewness of some variables, the variable transformation technique has been conducted and a transformed logistic regression is built. The logistic regression models perform better than tree model, while the non-transformed logistic regression model and transformed regression model are indistinguishable. The non -transformed regression model is recommended for this transportation problem	regression analysis, logistic regression, logistic distribution, proc hpsplit, data mining, response variable, missing value, data cleaning, data transformation, sample	10
3	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 function, sas programmer	16
4	A Little Stats Won't Hurt You	Nathaniel Derby	This paper gives an introduction to some basic but critically important concepts of statistics and data analysis for the SAS programmer who pulls or manipulates data, but who might not understand what goes into a proper data analysis. We first introduce some basic ideas of descriptive statistics for one-variable data, and then expand those ideas into many variables. We then introduce the idea of statistical significance, and then conclude with how all these ideas can be used to answer questions about the data. Examples and SAS code are provided. KEYWORDS: Descriptive statistics, Statistical significance, SAS. All SAS code used in this paper is downloadable fromhttp://nderby.org/docs/RUG09-IntroStats.sas	proc step, sample, histogram, proc univariate, outlier, box plot, bubble plot, data science, proc calis, proc freq	18
5	A SAS Macro to Compute Added Predictive Ability of New Markers in Logistic Regression	Kevin F. Kennedy, Michael J. Pencina	Prediction of dichotomous events is an important statistical concept. In clinical research, development of a disease or complication after a procedure are often endpoints of interest. However, this concept is multi-disciplinary. Many published models exist to predict a dichotomous outcome, including a model to predict winners in the NCAA basketball tournament [1] and a recently published risk model to predict the likelihood of a bleeding complication after percutaneous coronary intervention [2]. It is important to note, however, these models are dynamic, as changes in population and technology lead to discovery of new and better methods to predict outcomes. An important consideration is when to add new markers to existing models. While a significant p-value is an important condition, it does not necessarily imply an improvement in model performance	sample, proc step, macro definition, logistic distribution, do loop, logistic regression, proc sql	9
6	Advanced Topics in ODS	Lauren E. Haworth	This paper represents part of the material that will be presented as In-Conference Training. The paper is a whirlwind tour of some of the most useful advanced techniques in `ODS`. The full training workshop will cover topics in more detail. Topics covered include: Tip sheets on new features inline formatting, RTF features: enhanced page numbers, hyperlinks, text, tables of contents, and RTF control words PDF features: bookmarks, enhanc ed page numbers, hyperlinks, text	proc step, ods pdf, ods rtf, footnote statement, options statement, title statement, proc format, proc print, sas base, ods html, anova, proc tabulate	20
7	An Example of Website "Screen Scraping"	Eric Lewerenz	Have you ever needed to collect information from a website without having to tediously cut-and-paste from several different web pages? This paper highlights a cobbled-together method the author used in solving a specific business problem. For beginner and intermediate SAS programmers, this paper may serve as an introduction to a wide range of different SAS functionality, including macros, regular expressions, the URL access method, the DO/%DO loop, `PROC TRANSPOSE`, and the INDEX and SUBSTR functions	proc step, index	8
8	Analysis of Breast Cancer and Surgery as Treatment Options	Beatrice Ugiliweneza	In this paper, we analyze breast cancer cases from the MEPS (Medical Expenditure Panel Survey), the NIS (National Inpatient Sample) and the Thomson Medstat Market Scan data using SAS and Enterprise Guide 4. First, we find breast cancer cases using ICD9 codes. We are interested in the age distribution, in the total charges of the entire treatment sequence and in the length of stay at the hospital during treatment. Then, we study two major surgery treatments: Mastectomy and Lumpectomy. For each one of them, we analyze the total charges and the length of stay. Then, we compare these two treatments in terms of total charges, length of stay and the association of the choice of treatment with age. Finally, we analyze other treatment options. The objective is to understand the methods used to obtain some useful information about breast cancer and also to explore how SAS and Enterprise Guide 4 can be used to examine specific healthcare problems	sas enterprise guide, proc means, logistic regression, proc step, bar chart, proc freq, logistic distribution, regression analysis	17
9	Analysis of Emergency Room Waiting Time in SAS	Brent Wenerstrom	Background: Life and death may be on the line for pa tients visiting an emergency department (ED). The time it takes a patient to see a doctor can be critical. This is made more difficult by the fact that waiting times have been increasing over the past ten years [2]. Objective: We would like to determine what factors impact the current wait ing in emergency departments through the use of Enterprise Guide . Methods: We are using survey data from the 2006 Ambulatory Health Care Data survey [1] conducted by the National Center for Health Statistics (NCHS) containing about 36,000 data records. We used linear regression to model our data with the help of the PR OC GLM function. Results: We found that self reported pain levels did not correlate with waiting time , but that ED prioritization, time of day of visit, arrival by ambulance and previous waiting times fr om the same emergency department correlated with waiting time	sample, regression analysis, proc step, proc sql, scatter plot, proc reg, sas enterprise guide, correlation, histogram	10
10	Analysis of Metabolic Disorder - Gout	Sireesha Ramoju	The purpose of this project is to examine the analysis of metabolic disorder called Gout. This disease is created by a buildup of uric acid crystals deposited on the articular cartilage joints, tendons and surrounding tissues. Medical treatment for gout usually involves Short-term treatment and Long-term treatment. Kernel Density Estimation is used to examine the ratio of the disease, medication and cost. The Logistic Regression and Linear Regression analysis are used to find the various demographic factors that affect the Gout disease. The data are from MEPS (http://www.meps.ahrq.gov). SAS software is used for the analysis of Kernel Density Estimation, Logistic Regression and Linear Regression	regression analysis, logistic regression, logistic distribution, sas enterprise guide, response variable, proc reg	30
11	Base SAS 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	14
12	Best Practices for Configuring Your I/O Subsystem for SAS9 Applications	Margaret A. Crevar, Tony Brown, Leigh A. Ihnen	The increased power of SAS 9 applications allows information and knowledge creation from very large amounts of data. Analysis that used to consist of 10s-100s of gigabytes (GBs) of supporting data has rapidly grown into the 10s of terabytes (TBs). This data expansion has resulted in more and larger SAS data stores. Setting up file systems to support these large volumes of data, as well as ensuring adequate storage space for the SAS temporary files can be very challenging. This paper will present some best practices for configuring the IO subsystem for your SAS9 applications, ensuring adequate capacity, bandwidth, and performance to keep your SAS9 users moving	dataset, error message	11
13	Clinical Programming at a Crossroads: Meeting Today's Challenges and Preparing for Tomorrow	Vid J. Polus	Consulting in the Clinical Programming world is more competitive than ever. The demand for a good programmer with clinical knowledge is always increasing. At times, it seems the ideal candidate for a position doesn't exist anywhere. What makes clinical programming so different, anyway? This paper will review how to recruit, identify and hire the best candidates for the position. Once they're in the fold, mentoring and training become the keys to long term retention. Personal growth and a sense of belonging ensure a happy consultant, and a happier client. Finally, there are times when you have to perform damage control with a particular consultant. A well designed and executed Action Plan can help strengthen your relationship with both the client and the consultant	clinical programming, sample	5
14	Communication-Effective Reporting with Email/BlackBerry/iPhone, PowerPoint, Web/HTML, PDF, RTF/Word, Animation, Images, Audio, Video, 3D, Hardcopy, or Excel	LeRoy Bessler	This presentation explores all the channels one can use to deliver information. It emphasizes communication effective design and construction. It is intended for new or experienced users of Version 9.1.3 or 9.2 of SAS , `SAS/GRAPH`, and `ODS`	proc step, proc print, bar chart, excel, proc template, proc gchart, output delivery system, ods pdf, proc freq, footnote statement, title statement, sas output, sample	79
15	Connect with SAS Professionals Around the World with LinkedIn and sasCommunity.org	Kirk Paul Lafler, Charles E. Shipp	Accelerate your career and professional development wi th LinkedIn and sasCommunity.org. Establish and manage a professional network of trus ted contacts, colleagues and experts. These ex citing social networking and collaborative online communities enable users to connect with millions of SAS users worldwide, anytime and anywhere. This paper explores exciting features found in both virtual communities. Topics include creating a profile and social network content, developing a network of friends and colleagues, joining special-interest groups, accessing a Wiki- based web site where anyone can add or change content on any page on the web site, sharing biographical information between both communities using a built-in widget, exchanging ideas in Bloggers Corner, viewing scheduled and unscheduled events, using a built -in search facility to search fo r desired wiki-content, collaborating on projects and file sharing, reading and responding to specific forum topics, and much more		10
16	CSSSTYLE: Stylish Output with ODS and SAS 9.2	Cynthia L. Zender	It has become the standard for most company Web sites to use cascading style sheets (CSS) to standardize Web site look and feel. Now, with `ODS` and SAS 9.2, you can design a single CSS file to be used with `ODS RTF`, PDF, and HTML. In the past, CSS usage was limited to files created with `ODS HTML`. This paper provides an introduction into the use of the new CSSSTYLE option in SAS 9.2. This option allows you to use cascading style sheet (CSS) style specifications for RTF and PDF files, in addition to HTML files. This paper will include a brief introduction to CSS syntax and some of the features, such as @media CSS sections, that are particularly useful when creating `ODS` output. Then, all the methods of using CSS with `ODS` are discussed, with an emphasis on the new CSSSTYLE option. In addition to these topics, a job aid will be provided that outlines the most commonly used CSS properties and property values	ods html, output delivery system, sas enterprise guide, ods pdf, sas 9	10
17	Dear Miss SASAnswers: A Guide to Efficient PROC SQL Coding	Jane Stroupe, Linda T. Jolley	She's back! Advice columnist Miss SASAnswers has received more questions from curious users-questions such as "What used all that CPU time?", "Which should I use: merge or join?", "Should I use a subquery?", and "Why should I learn SQL, anyway?" She will share her answers to help you harness the potential of Structured Query Language	proc sql, proc step, where statement, order by, group by, create table, inner join, dataset, select, options statement	14
18	Decision Making Using PROC OPTMODEL	Joseph Czyzyk	How do you plan for an uncertain future? A common approach for tackling this problem is to generate a set of possible outcomes for the future, commonly called scenarios. Each scenario has some probability of occurrence, and the decision-maker is responsible to use the scenarios to make a wise decision in the current period to be ready for the future. If the decision problem can be formulated as a linear program, the uncertainty can be captured in the model using a stochastic programming formulation. We will present how to use `PROC OPTMODEL` to write the linear programming formulation of a planning problem. We will show how scenarios can be written to capture the uncertainty in future periods of the planning horizon. The stochastic program will be formulated and solved using `PROC OPTMODEL`. We will show how optimizing over all of the future scenarios together at one time provides a better solution than optimizing over each scenario individually	odds ratio, proc step, linear programming	8
19	Demonstration of Organic Growth Modeling for B to B Marketing	Karen Ziton		response variable, data mining, predictor variable, likelihood function, output delivery system, proc hpsplit, proc logistic	15
20	Dietary Glycemic Load and Risk of Colon Cancer: A Guide to Data Management	Svetlana Zelenskiy, Lauren Byrne, Cheryl L. Thompson, Thomas C. Tucker, David Bruckman, Li Li	Data come in different file formats and before we can summarize and explore patterns in the data and draw any inferences, it is essential to 1) import data from an outside source and assign variable names; 2) merge several sections into a single data set; and 3) clean variables and create new variables. After these initial steps, we can proceed with 4) exploratory analyses and model building. This paper is geared toward graduate students and novice coders. The goal of this paper is to discuss effective methods to creating a data set ready to be used for exploratory analyses and model building	logistic regression, macro definition, proc step, logistic distribution, missing value, regression analysis, conditional logistic regression, macro variable, merge statement, proc freq, proc import, if-then-else, sample, proc logistic, set statement, excel, array, macro call, do loop, nominal variable, predictor variable, medical history, likelihood function, confidence interval	16
21	DO's and DON'Ts of Generating Performance Metrics	Michael C. Frick	The author draws on 10 years of working experience with both executive leadership and operational groups to provide a practical list of the do's and don'ts for generating performance metrics that will drive the desired improvements. A sample of topics covered are (1) the need to separate process improvement metrics (owned by planning) from process execution metrics (owned by operations), (2) the need for alignment of metrics with organizational boundaries and operational job responsibilities, (3) the balancing act between too many and too few metrics on a scorecard, (4) dangers of going to an automated electronic scorecard system, and (5) why medians are a "don't."	logistic distribution, key performance indicator, date variable, sample	7
22	Effective Use of RETAIN Statement in SAS Programming	Yi Zhao		retain, data step, out=	4
23	Eliminating Redundant Custom Formats (or How to Really Take Advantage of PROC SQL, PROC CATALOG, and the DATA Step)	Philip A. Wright	Custom formats are an invaluable asset to the SAS programmer. Their functionality provides for much more than simply a mechanism for explicitly labeling values in a dataset. There can be, however, a major limitation-the `DATA STEP` can only accommodate 4,096 formats at a time. It is unlikely that a SAS programmer would generate this many formats in code, but this is not the only method that generates formats. `PROC IMPORT` and other third party data conversion programs may well generate a distinct custom format for every variable in a data set, and data sets with more than 4,096 variables are not uncommon. Oftentimes, however, these formats can be quite redundant-the same coding scheme was used for many similar variables. Eliminating redundant custom formats may well get you below the 4,096 limit. `PROC SQL`, `PROC CATALOG`, and the `DATA STEP` are the tools that can be used to eliminate the redundant formats	proc step, user-defined format, proc sql, proc datasets, proc format, data step, create table, order by, work library, proc catalog, format statement, proc contents, proc compare, proc sort	18
24	Exploring DATA Step and PROC SQL Programming Techniques	Kirk Paul Lafler	Are you considering whether to use a `DATA STEP` or `PROC SQL` step in your next project? This presentation explores the similarities and differences between `DATA STEP` and `PROC SQL` programming techniques. Topics include IF- THEN-ELSE, SELECT-WHEN, and `PROC SQL` CASE expressions conditional logic concepts and constructs; and the techniques for constructing effective merges and joins. Attendees explore examples that contrast `DATA STEP` versus `PROC SQL` programming techniques to conduct conditional logic scenarios, one-to-one match-merges and match-joins, and an assortment of inner and outer join programming constructs	proc sql, if-then-else, proc step, inner join, character variable, missing value, create table, right join	14
25	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 will learn how these important tables and views can be accessed and applied using real-world scenarios	dictionary tables, proc sql, proc step, sas library, options statement, select	7
26	Fun with SAS Date/Time Formats and Informats	Randall Reilly	This paper was written as a tutorial for programmers new to SAS who encounter dates and times in a variety of formats. It was also written as a useful reminder to the more experienced SAS programmer on various ways to convert dates and times to SAS date/time values	sysdate9, datetime, time, time., datetime20.	5
27	Getting By with a Little Help from My Regular Expressions	Scott Davis	Much has been written about the power and flexibility of regular expressions. Regular expressions represent an entirely new drawer in the programmer's toolbox that provide yet another way of attacking a problem and developing a solution. Imagine a rather common scenario where, during a testing phase, output from the current run is compared to output from the previous run. If updates to the program do not pertain to a particular table, then the output from both runs should be the same. What about the logs though? Excluding things like dates, what happens when changes in the program result in vastly differing logs. Compare utilities cannot adequately match up these logs with the additional information. Enter some regular expressions along with metadata to help solve the comparison problem. The goal of this paper is to explore a particular application of regular expressions (the comparison of vastly differing logs). This paper assumes a basic understanding of regular expressions	error message, temporary array	5
28	Getting Correct Results from PROC REG	Nathaniel Derby	`PROC REG`, SAS's implementation of linear regression, is often used to fit a line without checking the underlying assumptions of the model or understanding the output. As a result, we can sometimes fit a line that is not appropriate for the data and get erroneous results. This paper gives a brief introduction to fitting a line withPROC REG, including assessing model assumptions and output to tell us if our results are valid. We then illustrate how one kind of data (time series data) can sometimes give us misleading results even when these model diagnostics appear to indicate that the results are correct. A simple method is proposed to avoid this. Examples and SAS code are provided. The `SAS/GRAPH` package is used in this paper but not required to use these methods, although `SAS/STAT` is required to usePROC REG. KEYWORDS: SAS,`PROC REG`, assumptions, residuals, time series. All data sets and SAS code used in this paper are downloadable fromhttp://nderby.org/docs/RUG09-PROCREG.zip	proc step, anova, proc reg, estimate, predictor variable, response variable, normal distribution, regression analysis	14
29	Getting from SAS 9.1.3 to SAS 9.2: Migration Tools or Promotion Tools	Diane Hatcher, Sandy McNeill	If you are running a metadata server in your SAS 9.1.3 environment, you must upgrade your metadata when you move to SAS 9.2. There are two possible approaches to take. The first approach is a migration, which will essentially copy your SAS 9.1.3 environment over to SAS 9.2 as part of your SAS 9.2 installation process. The second approach is to make a fresh start by installing SAS 9.2 first, then promoting specific content from SAS 9.1.3 afterwards. This paper will describe the tools, processes, and considerations for each approach. The goal is to help you make an educated assessment to determine which approach will work best for you	sas management console, sample, data cleaning, spk file	12
30	Getting Started with the Business Intelligence Tools from SAS	Ben Cochran	This in-conference course starts with a couple of SAS datasets and takes the user quickly through the necessary steps to generate reports in Web Report Studio. This is not intended to be a very comprehensive course, but rather a quick start at doing what is needed to get reports ready and place them on the Web. A quick look is given to SAS Enterprise Guide, SAS Management Console, SAS OLAP Cube Studio, SAS Information Map Studio, SAS Web Report Studio, and SAS Information Delivery Portal	sas enterprise guide, sas management console	2
31	Group Sequential Analysis Using the New SEQDESIGN and SEQTEST Procedures	Yang Yuan	In a fixed-sample clinical trial, data on all individuals are analyzed at the end of the study . In contrast, a group sequential trial provides for interim analyses before completion of the trial. Thus, a group sequential trial is useful for preventing unnecessary exposure of patients to an unsafe new drug or to a placebo treatment if a new drug shows significant improvement. This paper reviews basic concepts of group sequential analysis and introduces two `SAS/ST` A T procedures: the SE- QDESIGN and SEQTEST procedures. Both procedures are experimental in SAS 9.2. The `SEQDESIGN` procedure creates group sequential designs by computing boundary values with a variety of methods, including the O'Brien- Fleming, Whitehead, and error spending methods; it also provides required sample sizes	sample, sample size, alternative hypothesis, confidence interval, type ii error, null hypothesis, group sequential design, ods output, type i error, likelihood function, maximum likelihood estimation, ods graphics, z-test, proc step, output, proc calis, output delivery system	20
32	Handy Procedures to Expand Your Analytics Skill Set	Mary F. MacDougall	A good handyman can accomplish a lot with a general-purpose tool like a hammer or screw driver, but for some projects, it's critical to have a special-purpose tool like a pipe wrench in your tool box. `PROC REPORT` is great for everyday reporting tasks, but a senior analyst should also be familiar with procedures that are effective for specific data preparation and analysis tasks. This paper gives an introduction to my favorite Base SAS and `SAS/STAT` procedures: UNIVARIATE, RANK, TRANSPOSE, FORMAT, and SURVEYSELECT, with examples from the field of direct marketing. So the next time you need to Decile, Flatten, Bin, Sample, or Check for Outliers, you will know which `PROC` to start with	proc step, sample, proc univariate, proc rank, class variable, proc means, outlier, user-defined format, proc format, random sampling, proc surveyselect, by group processing, format statement, proc sql, proc transpose	9
33	High Performance Analytics with In-Database Processing	Stephen Brobst, Keith Collins, Paul Kent, Michael Watzke	A new era of high performance analytics has emerged. In-database processing for deep analytics enables much faster turnaround for developing new analytic models and dramatically reduces the cost associated with data replication into "shadow" file systems. This talk examines these trends and quantifies the impacts. We also provide best practices for execution of a phased deployment strategy of these capabilities	in-database processing, data warehouse, proc sql, data governance, parallel processing, proc step, structured data, data mining, proc freq	8
34	How to Maintain Happy SAS Users	Margaret A. Crevar	Today's SAS environment has high numbers of concurrent SAS processes and ever-growing data volumes. It is imperative to proactively manage system resources and performance to keep your SAS community productive and happy. We have found that ensuring your SAS applications have the proper computer resources is the best way to make sure your SAS users remain happy	sas batch, batch job, proc step, proc sql, dataset	15
35	Irritable Bowel Syndrome and Mood Disorders	Pedro Ramos	This case study describes the use of SAS technology in streamlining cross-sectional and retrospective case-control studies in the exploration of comorbidity of depression and gastrointestinal disorders. Various studies in Europe and America have documented associations between irritable bowel syndrome and psychological conditions such as depression and anxiety disorders; however, these were observational studies. Because it is impossible to randomize symptoms, it is difficult to isolate patients with these co-morbidities for randomized trials. Therefore, studies will continue to use observational data. In this study, all steps are conducted electronically in a rapid development environment provided by SAS technology. In addition, it examines the potential rate of health-care utilization particularly for GI disorders among individuals with depressive symptoms and anxiety disorders	data mining, proc freq, sas enterprise guide, sample, proc sql	6
36	List Processing Basics: Creating and Using Lists of Macro Variables	Ronald J. Fehd, Arthur L. Carpenter		macro put, macro variable, proc sql, proc step, call execute, macro call, macro loop, macro definition, %if-%then-%else, output delivery system, do loop, proc report, dictionary tables, data step, create table, proc sort, macro function	21
37	Mail Merge using SAS	Michael Stout	Have you ever wanted to add a formatted letter to the front of your SAS output? Using the SAS `ODS` facility is an excellent method for combining letters with SAS output. Creating letters using a word document and then collating them for small to medium mailings can be tedious and time consuming. SAS `ODS`, `PROC SQL`, `PROC IMPORT`, inline styles and Microsoft `EXCEL` come to the rescue and make it fairly easy to create standardized letters on the fly. This paper shows one method that may be used to dynamically generate letters while saving time and improving the quality and presentation of the reports being prepared for distribution	macro variable, excel, proc step, proc import, dataset, proc sql	10
38	Mixed Model Selection	George C. J. Fernandez	A user-friendly SAS macro application to perform all possible model selection of fixed effects including quadratic and cross products within a user-specified subset range in the presence of random and repeated measures effects using SAS `PROC MIXED` is available. This macro application, ALLMIXED2 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, proc mixed, repeated measures, covariance, proc step, covariance structure, restricted maximum likelihood, macro definition, correlation, spearman correlation, predictor variable, sample, akaike information criterion, data visualization, sample size, interaction effect, longitudinal data, scatter plot, excel, regression analysis	19
39	More Tips and Tricks for Creating Multi-Sheet Microsoft Excel Workbooks the Easy Way with SAS	Vincent DelGobbo	Transferring SAS data and analytical results between SAS and Microsoft `EXCEL` can be difficult, especially when SAS is not installed on a Windows platform. This paper explains how to use the XML support in Base SAS 9 software to create multi-sheet Microsoft `EXCEL` workbooks (for `EXCEL` versions 2002 and later). You will learn step-by-step techniques for quickly and easily creating attractive multi-sheet `EXCEL` workbooks that contain your SAS output, and also tips and tricks for working with the ExcelXP `ODS` tagset. Most importantly, the techniques that are presented in this paper can be used regardless of the platform on which SAS software is installed. You can even use them on a mainframe! The use of SAS server technology is also discussed. Although the title is similar to previous papers by this author, this paper contains new and revised material not previously presented	excel, xls, date variable, sas output, proc template, sas base, sysdate9, output delivery system, title statement, by group processing, proc print, ods xml, format statement, proc step, sample	20
40	No More Blue Screens - Running SAS on Windows Servers	Joanne Ellwood	Running SAS Foundation products on Windows Servers with many users can be very challenging. Users can experience slowdowns and suspended blue screens when trying to log onto the server. Runaway SAS sessions can consume large amounts of CPU while doing no real work. This presentation will show how SAS server administrators and users can manage these problems using standard Windows utilities, successfully avoiding the dreaded non- scheduled reboot of a SAS application server. Strategies discussed in the presentation may have application to other SAS installations such as BI Server	cpu time, dataset	3
41	Obtaining the Patient Most Recent Time-stamped Measurements	Yubo Gao	Each time when patient visited clinic, the clinic took several measurements, and the measurements usually varied with visits. Researchers sometimes are interested in the most recent time-stamped measurements even though they are taken in different times. Some paper, for example in [1], has tried to obtain the most recent measurements, but failed to report the associated time. This paper corrects that shortcoming in [1] by adding the corresponding date for each most recent measurement. This will tell more important information than that without dates	numeric variable, character variable	6
42	Outcome Research for Diabetic Inpatients with SAS Enterprise Miner 5.2	Xiao Wang	The main purpose of this paper is to evaluate and predict the diabetic inpatient outcomes in Medicare. In the study, we used data sets about inpatient claims, or beneficiary demography information for the year 2004, both of which come from the Chronic Condition Data Warehouse. In this study, we used the Text Miner node to generate procedure and diagnosis clusters, preparing for kernel density estimation of the total charges, association analysis of the various procedures and prediction of the outcomes. We also used the link graphs and the rules table in the association analysis and different kinds of predictive models to analyze the outcomes. We utilized the CATX function to put all possible diagnosis or procedure codes into one text string .We also used the RXMATCH function, Random Sampling, SAS SQL and Base SAS. Results show that many organ diseases and neurological disorders raise the costs of inpatient care	proc hpsplit, regression analysis, data mining, test set	21
43	Predictive Modeling in Enterprise Miner Versus Regression	Patricia B. Cerrito	We investigate the difference between regression models in `SAS/STAT` and compare them to the predictive models in Enterprise Miner. In large samples, the p-value becomes meaningless because the effect size is virtually zero. Therefore, there must be another way to determine the adequacy of the model. In addition, logistic regression cannot be used to predict rare occurrences. Such a model will be highly accurate, generally predicting all occurrences as non-occurrence. However, it will have no practical use whatsoever in identifying those at high risk. In contrast, predictive modeling in Enterprise Miner was designed to accommodate large samples and rare occurrences as well as providing many measures of model adequacy	regression analysis, sample, proc hpsplit, type ii error, neural network, type i error, data mining, roc curve, discriminant analysis, logistic regression, response variable, logistic distribution	27
44	PROC REPORT: Compute Block Basics	Arthur L. Carpenter	One of the unique features of the `REPORT` procedure is the Compute Block. Unlike most other SAS procedures, `PROC REPORT` has the ability to modify values within a column, to insert lines of text into the report, to create columns, and to control the content of a column. Through compute blocks it is possible to use a number of SAS language elements, many of which can otherwise only be used in the `DATA STEP`. While powerful, the compute block can also be complex and potentially confusing. This tutorial introduces basic compute block concepts, statements, and usages. It discusses a few of the issues that tend to cause folks consternation when first learning how to use the compute block in `PROC REPORT`	proc report, proc step, class variable, put statement, proc calis	17
45	Ranking Predictors in Logistic Regression	Douglas Thompson	There is little consensus on how best to rank predictors in logistic regression. This paper describes and illustrates six possible methods for ranking predictors: 1) standardized coefficients, 2) p-values of Wald chi-square statistics, 3) a pseudo partial correlation metric for logistic regression, 4) adequacy, 5) c- statistics, and 6) information values. The methods are illustrated with examples using SAS `PROC LOGISTIC` and GENMOD. The interpretation of each ranking method is outlined, and upsides and downsides of each method are described	partial correlation, logistic distribution, logistic regression, correlation, regression analysis, likelihood function, proc reg, interval variable, proc logistic, proc step, missing value, sample	13
46	Revolutionary BI: A Vision for Business Intelligence	Charles D. Kincaid		business intelligence, sas enterprise guide	7
47	SAS Macro: Symbols of Frustration? %Let us help! A Guide to Debugging Macros	Kevin Payton Delaney, Arthur L. Carpenter	Are you just learning to write MACROS? Are you baffled by when to use single quotes and double quotes - or one inside the other? Are you confused by when to use statements such as DO versus %DO? Want to know the difference between a GLOBAL and LOCAL Macro variable? Do you have macros that don't give the right results yet don't give error messages either? This workshop will introduce a few MACRO concepts that are often misunderstood. You'll also learn how to use options such as MPRINT and SYMBOLGEN, and statements such as `%PUT`, to help you determine why your macro doesn't run, or why it does run but gives you the wrong results	macro variable, do loop, macro loop, macro facility, symbol table, macro definition, macro put, %if-%then-%else, character functions, proc sql, macro function, macro quoting function, options statement, proc step, cpu time, missing value, put statement, sas log, data step, if-then-else	20
48	SAS Performance Tuning Strategies and Techniques	Kirk Paul Lafler	As SAS Software becomes increasingly more popular, guidelines for its efficient use is critical. Areas deserving special consideration include program execution, I/O, disk space, and program maintenance. A collection of techniques and sample code are presented to illustrate numerous practical methods for gaining efficiency while using SAS Software	proc sql, cpu time, dataset	6
49	Table Lookups: From IF-THEN to Key-Indexing	Arthur L. Carpenter	One of the more commonly needed operations within SAS programming is to determine the value of one variable based on the value of another. A series of techniques and tools have evolved over the years to make the matching of these values go faster, smoother, and easier. A majority of these techniques require operations such as sorting, searching, and comparing. As it turns out, these types of techniques are some of the more computationally intensive, and consequently an understanding of the operations involved and a careful selection of the specific technique can often save the user a substantial amount of computing resources. This workshop will demonstrate, compare, and contrast the following techniques: IF-THEN, IF-THEN-ELSE, SELECT, user defined formats (PUT function), MERGE, SQL joins, merging with two `SET` statements, using the KEY= option, use of ARRAYS, and Key-Indexing	proc sql, by group processing, lookup, proc step, merge statement, data merging, if-then-else, put statement, set statement, dataset, key=, cpu time, select	10
50	The Impact of the Food Safety Information on U.S. Poultry Demand	Lijia Mo	The impact of poultry product recall events on consumer demand in the USA was tested empirically for the four major categories of poultry: broiler (young chicken), eggs, turkey and other chicken (mature or non-broiler chicken). FSIS recall and the MEDIA recall impacted only turkey, and demonstrated that consumers of turkey were a special behavior group in poultry consumers. JEL Category: D-12, Consumer Economics-Empirical Analysis; I-18, Government Policy-Regulation-Public Health	price elasticity, proc means	9
51	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, data step, sample, date variable, character variable, sas log, cpu time, delimited file, dataset	17
52	The Super Genius Guide to Generating Dummy Data	Brian K. Varney	A common necessity in development and programming is having representative data to program and develop against. Situations that can hamper this could be that the data has not been collected yet, the data is too sensitive to share or just lack of resources to prepare and provide the data from the source data tables. This paper is intended to provide methods to generate representative data whether one has the project data or only metadata in such a way that sensitive data is not revealed	excel, sample, proc sql	5
53	Things Dr Johnson Did Not Tell Me: An Introduction to SAS Dictionary Tables	Peter W. Eberhardt	SAS maintains a wealth of information about the active SAS session, including information on libraries, tables, files and system options; this information is contained in the Dictionary Tables. Understanding and using these tables will help you build interactive and dynamic applications. Unfortunately, Dictionary Tables are often considered an 'Advanced' topic to SAS programmers. This paper will help novice and intermediate SAS programmers get started with their mastery of the Dictionary tables. Ever needed a list of the tables (datasets) in a library? How about the columns (variables) in a table? Need to make sure you reset any titles after you run a report? Got some pesky warning messages in your SAS log you would like to clean up? Sure, you can look them up in the table and column properties in the explorer window. Or you can run a `PROC CONTENTS` and check the listing. And of course you can ignore the warnings and errors in the SAS log since they 'almost always appear'	dictionary tables, proc sql, proc step, sas log, data step, sas programmer, sashelp, sas library	15
54	Time Series Analysis 101: an introduction using Base SAS and SAS STAT	David J. Corliss	The SAS system has many powerful tools for modeling events that change over t ime. This class offers an a basic introduction to time series analysis, what it can you for you and how to get the procedures in Base SAS and SAS Stat up and running. Techniques covered in this paper include Regression on Time Series , Autoregressive and Moving Average models. Source code supporting basic ARIMA models - both Autoregressive and Moving Average - using only Base SAS and SAS STAT are given. The strengths, weaknesses and optimal situations for each method are compared. Additional capabilities ava ilable with the SAS add -on ETS, including `PROC ARIMA` and `PROC` FORECAST, are briefly described	time series analysis, moving average, regression analysis, sas base, proc step	7
55	Turn-Key Performance Metrics using Base SAS and Excel VBA	Michael C. Frick	In today's competitive environment, everyone is being asked to do more with less. Here I describe a turn- key process which automatically produces weekly and monthly performance metrics with virtually no manual intervention. At the push of a button, base SAS is used to assemble the data and then dynamically generate custom VBA source code to produce approximately 200 `EXCEL` reports. As there is some initial set-up time, the methodology would be most useful to those who need to repetitively generate lots of tables and charts that have similar structure with a minimum of ongoing manual effort	excel, macro definition, txt, sas base, bar chart, dataset, sample, calendar year, data step, macro call, macro variable, sas programmer, %if-%then-%else	12
56	Using AJAX and SAS Stored Processes to Create Dynamic Search Suggest Functionality Similar to Google's	Jeffery A. Fallon	This paper shows SAS application developers the steps involved in using SAS Stored Processes to develop a web application with search suggest functionality. After a brief introduction to AJAX, the reader is given a guided look at how to call a SAS Stored Process from JavaScript using the XMLHttpRequest object. The paper then discusses the Stored Process code behind the search suggest functionality, and concludes with a discussion of performance and scalability	macro variable, sample, proc sql	17
57	Using Base SAS and SAS Enterprise Miner to Develop Customer Retention Modeling	Rex Pruitt	In this paper I will describe how to develop the components necessary using SAS tools and business analytics to effectively identify a "Good Customer."	sas base, proc step, data mining, proc tabulate, customer retention, correlation, proc print, ks statistic, proc surveyselect, excel, sas programming, sample	17
58	Using graph template language to customize ODS statistical graphs	Dongsheng Yang, Anne S. Tang	Different from the traditional `SAS/GRAPH` in SAS 9.2, `ODS` statistical graphics from procedures provide an easy and quick way to explore and visualize data. However, if you want to generate high quality plots for reports and publications, graph template language is a powerful tool to customize and build stand-alone `ODS` statistical graphs. This paper describes how to output SAS codes and data objects of an `ODS` statistical graph generated from a statistical procedure, and then use graph template language to customize graph templates and style templates. An example on customizing Receiver Operating Characteristic curves (ROC) step-by-step is provided	graph template language, roc curve, proc step, output delivery system, proc template, proc sgplot, sas log, logistic distribution	8
59	Using PROC CALIS and PROC CORR to Compare Structural Equation Modeling Based Reliability Estimates and Coefficient Alpha When Assumptions are Violated	Fei Gu, Todd Little, Neal M. Kingston	`PROC CORR` is widely used to calculate the Cronbach's α, and it has been described as a lower bound for test reliability. However, previous research has shown that when certain assumptions are violated coefficient α can overestimate or underestimate reliability. Raykov has shown that structural equation modeling can be used to estimate reliability. This research illustrates Raykov's SEM method in `PROC CALIS` and shows that under certain violations of assumptions coefficient α estimates can show a substantial positive bias in the some extreme circumstances, and the magnitude of bias of coefficient α estimates are larger than that of structural equation modeling based reliability estimates	correlation, proc step, proc calis, proc corr, covariance	10
60	Using PROC OLAP to Build Cubes with NON-Additive Measures	Ben Cochran	Most of the time, OLAP cubes are built from data that has additive measures, meaning that as you drilldown, the sum of all the lower levels will add up to the value at the highest level of the hierarchy. This is not always the case. Sometimes applications need drilldown capabilities on data where the measures are non-additive. And, sometimes data is additive in one dimension, but not another. Take for example, a car leasing company that has 2,000 cars to lease. They want to build a cube with two dimensions: Time and Geography. Across the Geography dimension, the number of cars is additive. Let's say that the levels in the Geography dimension are: Company, Region, State and City. At the Company level, the number of cars is 2,000. When we drilldown to the Region level, the total at all the regions adds up to 2,000. When we drill down to the next level (State), the total number of cars in all the states adds up to 2,000. etc	proc step, proc print, dataset	12
61	Using SAS as an Archival Repository for DB2 under z/OS (or other DBMS)	Rich Morris	Your DBA says that the older data has to be removed from the data base in order to make room for new data, but the Legal Department says that you still need to be able to access the older data. What do you do? Copying the data from DB2 to a SAS set is easy enough - but how do you keep track of where it went? And how do you get the data back again? What happens when you need to combine data from the live data base with data from the archives? This paper discusses the reasoning and code that went into building an in-house DBMS archival/retrieval process at Progressive Insurance. This process makes extensive use of macros, and the general methodology is currently being used to archive data from at least five DB2 data warehouses (and one SQL Server database)	macro definition, macro variable, proc sql, macro loop, %if-%then-%else, character variable, numeric variable, create table, create view, variable, length statement, proc step	22
62	Using the Data Step's ATTRIB Statement to both Manage and Document Variables in a SAS Dataset (lightly)	Philip A. Wright	There are several different ways in which to order variables in a SAS dataset. Some of them are quite similar, one is dangerous, and each of them must be used prior to a set, merge, or update statement. One of these statements- the attrib statement-can serve the roll of several other statements, is not dangerous, and can actually serve as light documentation in `DATA STEP` code. Generating a complete attrib statement for a large data set, however, can be a daunting task. The use of a macro that generates a complete attrib statement for a previously-generated data set can be of great use to manage the data set while the `DATA STEP` is further developed. Using the macro to generate a complete attrib statement and subsequently using the generated attrib statement to order/re-order and aid the conversion of variable types will be demonstrated	attrib statement, data step, numeric variable, proc step, label statement, character variable, proc sql, dataset, set statement, update statement, data merging	8
63	Visual + Detail = Effective Communication: Web-enabled Graph + Spreadsheet, Using SAS/GRAPH, ODS, PROC PRINT, and Excel	LeRoy Bessler	This is a tutorial on communication-effective design, construction, and delivery of visual reports, for new or experienced users of either Version 9.1.3 or Version 9.2 of	proc step, excel, macro variable, line chart, proc gplot, ods html, sas enterprise guide	35
64	Visualizing Key Performance Indicators Using the GKPI Procedure	Brian K. Varney	The `GKPI` procedure is new in SAS 9.2 `SAS/GRAPH`. This new procedure can be used to create graphical key performance indicator (KPI) charts which include sliders, bullet graphs, dials, speedometers, and traffic lights. This paper is intended to serve as an introduction to the `GKPI` procedure by discussing the syntax and demonstrating examples. In addition, this paper will discuss how results from the `GKPI` procedure can be integrated into existing SAS environments	key performance indicator, business intelligence, sas enterprise guide, proc step	9
65	When the List Grows Too Long: A Strategy to Utilize Freeform User Input in Your SAS Stored Process Web Applications	Jeffery A. Fallon	In web based reporting applications, we often allow users to select the values for which they want to see results from a list of values. With this approach there are two problems that immediately surface. The first is that lists become unwieldy beyond around 1000 values. The second is that some users have a large number of values that they want to select. Freeform text input is a viable option, but application developers are often reluctant to yield the control that a list gives them over of their application input. In addition, developers are often fearful of what users might throw at their applications. This paper shows developers how to use SAS text processing functions to powerfully transform even the scariest freeform text into application ready input	compress function, tranwrd function	4
66	Where Does This Where Go?	Scott Davis	The clever folks at SAS have given the user community a variety of ways to accomplish tasks. Being the creative bunch that we are, SAS programmers have yet to tire of discovering and using different paths to get to the same end. In that spirit, this paper focuses on a very popular SAS statement, the WHERE statement, and areas that can pose a problem when it is combined with `PROC SORT`. In a typical clinical study (read: ALL) there will be adverse events. Many times programmers will find themselves doing validation on an adverse event table that either they created or was created by someone else. We all know the power of the NODUPKEY option in `PROC SORT`, and countless experts have forewarned about the dangers that come with its use. For those of us who have heeded those warnings and proceeded with care, it turns out that another danger awaits when using a subsetting WHERE clause in your `PROC SORT`	where statement, proc sort, proc step, adverse events, data cleaning, proc freq	4

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