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NESUG 1991 Papers - Index

59 papers. Source: NESUG (Northeast SAS Users Group) conference proceedings.

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1 A Blueprint for Interactive PC-Based Application Development Aiman M. Zeid catalog, sas output, proc step, macro variable, dataset, proc printto, filename statement, libname statement, macro definition, options statement, sas base, batch job 10
2 A Comparison of Conversational Programming Tools in Version 6 of the SAS System Roger Staum Conversational programs pr~t users for Information, read their response, and continue processing based on that rnponse. Version 6 of the SAS System provides 11 n...t>er of tools that applIcations developers can use to build such programs. These tools are: OATA step windows Macro w i ndows SASIFSP software SAStAF software This paper surveys these tools, Roger Staun American Cyanamid Co. Identifying the strengths and weaknesses of each, and the types of appl !cations to which .they are best suited. Exa~les, containing both scr!!!!n displays and sourc!! code, as well as d!!sign and progranming tips, wi It be includ!!d in th!! oral pres!!ntation proc step, sas base 6
3 A Cook's Tour of SAS Formats and Informats Ralph W. Leighton proc step, proc format, proc print, format statement, decimal, input statement, flat file, numeric variable 8
4 A Programmer's First Experiences with a Real-Life Proc Report Application Lonni Schulz proc report, proc step, numeric variable, proc print 7
5 A SAS/IML Routine to Calculate SPMR Relative Risk Confidence Bounds Jimmy Thomas Efird relative risk, macro definition 2
6 A Tale of Two Releases: Benchmarking the Performance of the SAS System Release 6.06 against Release 5.18 Michael A. Raithel dataset, cpu time, bufno, bufsize, batch job, index creation, libname statement, memory usage, sas library, sas programmer, proc step, options statement 10
7 All About Strings Rhena Seidman character variable, data step 5
8 An Auto-Encoding System for Adverse Reactions Using the WHO Dictonary Denis Michel dataset, macro definition, %if-%then-%else 5
9 An Introduction to SAS Functions Ronald P. Cody character variable, put statement, sas function, sysdate9, numeric functions, input statement, numeric variable, date variable, mdy, verify, string functions, datepart, lag function 10
10 Analysis of Designs with Detached Control Groups Robert M. Hamer dataset, response variable, proc step, main effect, anova, proc glm, proc genmod 7
11 Analysis of Higher-Order Crossover Trials Jonathan B. Skinner correlation, mse 5
12 Annotation in SAS/GRAPH: An Introduction to Advanced Graphics Irene Mendelson Dressing up your SAS/GRAPH output with annotation is not as difficult as you might think. The key is to look at the graph you eventually want and to divide it into two pieces, the basic graph and the extras, or the annotation. This paper will present a strategy for annotation design that focuses on answering five key questions. This strategy will be applied to three categories of annotation: placing text; adding custom legends; and drawing lines bar chart, proc calis 4
13 Building a SAS Front-End using the Command Definition Utility Hallett German proc step, error message 4
14 Building Macro Based Systems Howard Levine macro variable, keyword parameter, %macro parameters, stored macro, proc step, macro definition 9
15 Coding A User Application into the Executive Information System of SAS/ASSIST Software Linda P. Atkinson dataset, proc step 6
16 Comparing PROC SQL to Standard SAS Coding Techniques Howard Levine proc sql, proc step, format statement, data step, proc format, proc sort, create table, proc print, put statement 6
17 Creating a Developer's Toolkit: Facilitating the Development of SAS/AF Applications Daniel Gronell This paper considers application development techniques for the SAS/ AF product under version 6. It is suited to developers having moderate experience with the SAS/AF product, SAS Screen Control Language (SCL), and SAS macros. It analyzes several tools in use at Lederle, and its intent is to give readers the beginnings of a toolkit of their own, and to provide some insight into toolkit development if-then-else, macro variable, sysrc, macro definition, macro call 7
18 Customized In-House Training with a SAS Trainer's Kit Marcia S. Murto data wrangling, flat file 5
19 DATA Step Reporting Marge Scerbo put statement, file statement, proc step, set statement, by group processing, options statement 7
20 Efficient Programming Revistited: A Review of Performance-Enhancing Features in Version 6.06 Frank C. DiIorio,
Paul Dalberth		 cpu time, proc step, numeric variable, where statement, compress=yes, character variable 9
21 Efficient SAS Software Programming: A Version 6 Update Jean Hardy Most papers exploring efficiency techniques in the SAS software have been concerned by version 5. Surprisingly few addressed the efficiency of version 6.06 features: WHERE processing, indexing and compression of SAS files, the SQL procedure, the use of engines. Some of these features decrease significantly programming and debugging time, although they can increase other ressources. This paper replicates, with release 6.06, some efficiency tests previously done with version 5 and adds ones targeted toward the new features dataset, where statement, proc step, character variable, subsetting if, proc sql, array, cpu time 5
22 Efficient Use of Numeric and Character Data Types Una Smith A basic understanding of data types in computer pro gramming languages is necessary for the efficient con struction of both data sets and programs in the SAS1 System. This paper addresses the efficient use d nu meric and character data types at two levels of project development: initial planning and ongoing mainte nance. For researchers and planners. some general con cepts of good data design are outlined. with an em phasis on data types; these concepts should help you to design data sets that are both clearer and easier r.o work with. SAS users who have not agonized over rloe choice of numertc or character variables rna\' dis cover that there are good reasons for exercising care in this matter. Those who hat·e may find a review of the major issues helpful as they learn how to use the powerful SAS DATA STEP more efficiently. For the data base 'manager. some practical strate gies for working around sub-optimal types are de scribed numeric variable, sysdate9, date variable, data step, macro variable, put statement, input statement, length statement 9
23 Efficient Use of SAS Institute's Technical Support Mason B. Nichols,
Sally J. Painter		 In order to support the SAS' System effectively at your s1te. you must be famlliarw1th the suooort servces prov<lecl by SAS lnsttute. This paper discusses botn types of support avalable: telephone support the Online Customer Support Facollty. Utilizing these services to tneir best advantage will make on-site support more efficient. In addition, lnstitute-suoplied tools and ser vu:es. suCh as the SAS Notes data set and the sample hbrary, are diScussed sample, options statement, sas base 3
24 Fuzzy Merges (How to Make MERGE Really Work) Jack N. Shoemaker SAS programmers often need to join the data of two or more data sets to produce a new data set suitable for analysis and display. if both, or all, of the data sets contain a common set of by-variables, and all but one of the data sets contain unique combinations of values of the by-variables, then the MERGE statement will work fine. When this is not the case, alternative action is reqllired. This paper britifly e:uunines the MERGE process and why it fails in cenain situations. Then it describes a variety of techniques for resolving the MERGE problem arising from a situation common to the insurance indllStry: linldng together data sets which describe populalions llSing an identification number and a pair of date values to associate an effective date range. 1he problem is treated generally followed by an example from the health insurance field data step, set statement, raw data, merge statement, retain, missing value 6
25 Graphical Evaluation of Product Characteristics Melissa A. Durfee Utilizing SAS/GRAPHTM, many aspects of product characteristics may be displayed and analyzed. Besides using regression plots to examine relationships between variables, contour and 3-D response surface plots may be constructed on two independent variables. In designed experiments, potentially significant factors may be indicated by Bayes posterior plot of effect estimates coupled with a cube plot to examine the response. In a Taguchi experiment, the optimal design may he identified through a main effects plot using the appropriate signal-to-noise ratio. In addition, the nature of product variation may be assessed using multi-vari (multivariate) plots. Bv interfacing SAS/GRAPH with SAS/QC procedures, process stability and caiJability may be evaluated. These graphical applications facilitate and simplify the use of statistics in industry regression analysis, proc step, main effect, contour plot, predictor variable, response variable, histogram 6
26 How to Hire a Consultant Robert Virgile sas consultant, data wrangling 3
27 Information Systems Design and Programming in a Health Services Research Environment Richard N. Lieberman Much of the literature devoted to informatiqn system design and programming focuses on the development and maintenance of production systems. While the research community actively uses SAS and other applications software, limited consideration is often given to system design issues. This is due to the frequent lack of a systems development life cycle, inadequate budgets, tight deadlines, and a "use-it-once-and-throw-it-away" mentali ty. Developing analysis files and information systems to support health services research is even more difficult, due to the complexity and depth of the subject matter, the free-text nature of most clinical information, and the liberal (sometimes obsessive) use of jargon. This paper will assess the differences between research and production applications with a focus on health servic es research. The role of SAS and other applications software in both environments will be compared and contrasted proc step, data dictionary 5
28 Interactively Obtaining a Small Amount of Data: A Comparison of Alternatives Michael J. Kozakiewicz There are at least seven ways in which a prognunmer can obtain a small amount of information from a SAS"' user interactively. These mclude DATA STEP wmdows, Macro windows. SASIA?, SASIFSP' and others. The tradeoffs involved in selecting one method over another include tnmsponability, esthetics. field auditing ease, etc. This paper discusses seven ways to accomplish the task. SAS code samples for five of the seven ways are included dataset, variable 5
29 Interfacing REXX and SAS Chris Whitley sysrc, macro definition 6
30 Introduction to Arrays and Array Processing Earl R. Westerlund Arrays are a very useful part of the sAS DATA STEP. With arrays, you can do repetitive processing on a number of SAS variables. You can also set up temporary arrays to hold working values that you would not want stored in a data set, or to hold constant values. This paper will begin with a simple inuoduction to ar rays: what they are good for, what they can't do, and how to set one up. From there, we will discuss how to use them to group sets of SAS variables and to set up tempo rary worlc spaces. Finally, we will discuss some more ad vanced features of arrays, including multidimensional ar rays, amy bowtds, and related DATA STEP fwtctions. This talk is intended for SAS users who have some expe rience with the DATA STEP, and little or no experience with arrays. Why Use Arrays? Sooner or later, everyone who does DATA STEP program ming encounters a situation where a group of variables must be processed in the same way array, variable list, dataset, array bounds, variable, do loop, temporary array, proc step 4
31 Introduction to Basic but Useful Macros Susan Godwin macro variable, macro definition, proc step 1
32 Introduction to Efficient Programming Techniques David S. Septoff,
Paige Valentine-Query		 Writing efficient SAS * code is a major concem of most SAS pro grammers. This paper discusses how you can improve the affi· ciency of your SAS programs simply by changing your programmong techniques. In some cases. dapending on your computing envoron ment. certain techniques may or may not be advantageous. After datermining which programs can be more efficient, you must decide which methodS of programming will be most effective cpu time, proc step, dataset, pdv, proc sort, options statement, by group processing, character variable, if-then-else, numeric variable, set statement, input statement 5
33 Is A Picture Really Worth a Thousand Words? James A. Sannerud The abundance of computer hardware and the power of statistical software have simplified the time consuming process of creating presentation quality graphics. SAS/GRAPH* provides the SAS8 software user the ability to produce high quality graphical output. Unfortunately, hardware and software cannot compensate for a lack of skill in graphical design. Unless the analyst has an appropriate understanding of the principles of effective graphical design, the presentation of the data may not convey the intended message. A pooriy designed or misleading graph is equivalent to a grammatical error in speech or text. This paper explores the subject of graphical design and discusses both effective and ineffective examples of each bar chart, missing value 5
34 Job Submission Menu (JSM): A Programming Tool to Standardize Clinical Trial Summarization Mark Becker,
Iraj Mohebalian,
Lora H. Schwab		 A Job Submission Menu (JSM) is described which aids in standardizing the SAS programming that lists, summarizes, and analyzes the data from clinical trials. Design objectives of the system include: self documentation of clinical trial programming, simplified batch and interactive job submission of SAS programs within the IBM/MVS operating system, a library system to document all clinical drug projects. minimization of duplicate programs across clinical studies, potential Computer-Assisted New Drug Application (CANDA) uses. The system was designed with flexibility to handle both standardized and non standardized programming. After over six years of use the system has gained dramatically In users and is credited with facilitating the evolUtion of standardization. JSM is compared to one other clinical trial, macro variable 6
35 Macros for User-Friendly Systems Robert Virgile proc step, dataset, macro facility, call symput, macro loop, macro variable, numeric variable, proc reg, sample, macro definition, %if-%then-%else 8
36 Overview of ETS Procedures for Business Forecasting Jeffrey Jarrett ETS procedures are presented in order to illustrate their use for the forecasting and prediction of numerical information used for strategic planning in business, industry, and government. The purpose of this session is to illustrate how a forecaster and planner can produce numerical forecas(s. In particular, you will see how to generate simple and economical predictions based on exponentially weighted moving average (EWMA), seasonal EWMA methods, seasonal-trend EWMA methods, and stepwise autoregressive methods. Examine SAS methods for decomposing time series to produce seasonally adjusted data. Fit and forecast multiple linear regression models with autocorrelated errors, heteroscedastic errors, handle missing values and produce prediction intervals. The identification, estimation and diagnostic testing steps of univariate autoregressive-integrated-moving average time series methods developed by Box and Jenkins time series analysis, regression analysis, proc esm, proc step, proc forecast, autocorrelation, correlation, proc reg 4
37 PIAF-A Real End-User Computing Environment Paul Proulx,
Nancy Faille		 This paper describes PIAF (Product Integrity Analysis Facility), a menu driven end-user computing system at Sikorsky Aircraft. a division of United Technologies Corporation. The system provides a tully integrated environment tor accessing and analyzing the Quality data that are generated during the manufacture of Sikorsky Aircraft helicopters. The system was developed in 1988 in the end-user community within the Product Integrity department. It has become an integral part of the Quality Engineer's toolkit, providing a common set of analysis tools for multiple company databases. From an initial 2o-user base within Product Integrity, PIAF has grown to support 125 users from 10 different departments. The system is developed under the IBM MVS system using ISPFIIP as the user interface, rso CLIST for system programming, and the SAS System lor all application programming. Extensive use is made of the base SAS software, SAS/GRAPH, and SAS/FSP proc step, macro definition, numeric variable, data science, proc freq, macro variable 6
38 Postioning Variance and Comparing Treatment Means with CONTRAST and ESTIMATE Kenneth S. Yourstone Confusion about post-Analysis of Variance statistical tests has led to undue reliance on mean separauon techniques such as Duncan's multiple-range test. Combined with careful pre-experimental planning, partitioning the treatment and interaction .degrees of freedom (dO into single df or groups of df IS th~ more powerful analysis. Partitioning df is always poss1ble m structured factorial experiments or if the treatment cons1sts of several levels of a quantitative variable. Examples using the CONTRAST and ESTJMA TE statements in PROC GLM are given which illustrate variance partitioning and mean comparisons for one-way and factorial experimental designs proc genmod, proc glm, proc step, regression analysis, main effect, residual 6
39 PROC GREPLAY: A Valuable Tool for the SAS Graphics User Kimberly A. Mitchell,
C. Michael Dunham,
David R. Gens		 PROC GREPLAY is a multipurpose presentation tool for creating ·images from information generated by SAS graphics procedures. Greplay allows the user to view and modify graphics catalogs which contain the image producing details. The user can modify the names and colors of graphs, select specific graphs for viewing, move graphs between catalogs, delete graphs, display more than one image per screen, and transmit image producing information to different drivers. Once the catalog is created, Greplay eliminates the need for repetitious creation of data steps and graphics procedures when multiple image visualization is necessary. The user has the option of using interactive or batch mode environment. PROC GREPLAY has proven to be a valuable technique for storing, modifying, and presenting SAS generated graphics display information proc step, proc greplay 5
40 PROC SUMMARY and PROC FORMAT: A Winning Combination Alan Dickson proc step, proc means, proc format, proc print, class variable 7
41 Profiling and Rebundling Medical Care Claims Using the FORMAT Procedures and Macros Michael L. Davis Medical care providers commonly resort to billing separately for procedures which should be billed as a single CPT code. Writing a program to profile or rebundle medical claims is complicated by problems such as the: · need to evaluate a random nwnber of claims simultaneously · permutations of logic used to recombine claims · characteristics of profiles or bundles that are not composed of mutually exclusive CPT codes An elegant solution to these difficulties is to use some of the subtle features of tlte FORMAT Procedure and Macros. The following describes how to write a program which may be used under Releases 5.18, 6.04, or Version 6.06 proc format, audit trail, type i error, macro facility, macro definition 6
42 Pushing An Old Product Past its Limits, or How to Get Beyond the Standard Performance of PC SAS John W. McCue,
Robert A. Butler,
Philip Hamm		 mis, dataset 5
43 Reading Complex External Files or "Getting the Report Out When You Don't Have a SAS Dataset" Clinton S. Rickards SAS has very powerful tools for reading a wide variety of external files, from the most rudimentary to the most complex. This paper will introduce some DATA STEP techniques for reading standard external files that will input statement, pointer control, filename statement, dataset, infile statement, variable, formatted input, list input, variable list 8
44 SAS as a Systems's Adminstrator's Tool: A Tutorial David M. Clayton input statement, dataset, proc step 6
45 Screen Control Language: Running Behind the Scenes Daryl R. Hoffman data integrity, data step 5
46 So ... You Want to Become a SAS Consultant Jules Bosch The decision to become a SAS consultant is a daunting one; It may be precipitated by any number of events. These might Include the need for a mid-career employee to escape an unsatisfactory job or a dead-end position. Possibly the desire to continue one's professional career while also providing family care has lett an individual with the need to work independently of the more formal corporate structure. Whatever the reason, the formula for becoming a successful SAS consultant requires three primary ingredients: a marketable skill, the desire to be a specialist. and persistence. This paper addresses the many aspects of becoming a SAS consultant - the mundane as well as the exciting - with particular emphasis on the three fundamental ingredients mentioned above sas consultant, sas programming 8
47 Tests for Normality and Other Goodness-of-Fit Tests Ralph D'Agostino Jr.,
Albert J. Belanger		 sample, normal distribution, lognormal distribution, exponential distribution, proc step, proc univariate, proc rank, logistic distribution 7
48 The Adventure Continues: Using CLIST, DOS Batch Language, DCL, and REXX Command Languages in the SAS Development Cycle Hallett German 6
49 The Consortium Training Alternative: Getting the Best Training for Less Peter Prause In June, 1990, The Hartford Area SAS Users Group (HASUG) realized there was little SAS training offered at any of their member companies. As a result, HASUG began an aggressive program to sponsor SAS Institute consortiwn courses. These courses saved member companies more than $200,000 while training almost 300 students. Along the way, HASUG learned about the requirements of supporting consortiwn classes. This included the need to publicize courses and to develop a sense of shared ownership. We also learned that successful consortiwn courses require attention to a myriad of details. Last, we found that consortiwn education can be an important function for local SAS user groups. This paper gives an overview of all SAS training alternatives and provide a flowchart to help guide you to the appropriate training solution. This paper describes techniques that may be used to run courses more smooth1y batch processing, macro definition 5
50 The Progression and Integration of the CANDA at Merck Nancy Bauer,
Wanda Bidlack,
Dan Himmel,
Bruce Binkowitz		 dataset, clinical trial, proc step, data cleaning 6
51 Two Measures of Rating Reliability Peter P. Gaccione proc step, proc glm, anova, proc genmod, weighted kappa, proc sort, proc mixed, correlation, proc freq 7
52 Two-Dimensional Table Look-up Procedure Claude Biggs A two-dimensional table look-up method is different from the other traditional methods such as the general table look-up, hashing, and binary search. The two-dimensional table look-up predictor variable, proc format, calendar year, dataset, response variable, macro definition 6
53 Using SAS Character and List Functions to Extend the SAS Systems Par I. Data Step Applications Sigurd W. Hermansen character variable, data step, character functions, length statement, dataset, out=, variable, numeric variable, macro definition 6
54 Using SAS Macros to Generate Code (So Your Program Can Figure Out What to Do for Itself) Nancy K. Blood proc means, macro variable, macro definition, proc step, %if-%then-%else 4
55 Using SAS Procedures as Data Checking Tools Paula B. Podolnick proc step, missing value, proc means, interval variable, proc freq, proc univariate, variable list, sample, proc tabulate 5
56 Using SAS Statistical Procedures in Medical Research Joe Cholewczynski proc freq, proc means, proc step, survival analysis, clinical trial, life table 7
57 Using SAS to Develop a Microcomputer-Based Decision Support System Alberto Cordoba dataset, regression analysis 4
58 Using Spreadsheet Systems to Complement SAS/STAT Edward Lee,
Kendall Bryant,
Mary-Ellen Power		 cohen's kappa, excel, dataset, covariance, regression analysis 8
59 Using the Annotate Facility to Enhance Your SAS/Graph Output Michael A. Walega proc step, data step 6

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