MWSUG 2017 Papers - Index

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

#	Title ×	Authors ×	Abstract ×	Keywords ×	Pages
1	A Hands-On Introductory Tour of SAS ODS Graphics	Ted Conway	You've heard that SAS `ODS` Graphics provide a powerful and detailed syntax for creating custom graphs, but for whatever reason still haven't added it to your bag of SAS tricks. Let's change that! Workshop participants will quickly gain experience creating a variety of charts by using SAS PC to explore, run, and modify a number of SAS `ODS` Graphics code examples. An `EXCEL` -based workshop guide containing all the code and sample output for the exercises will be provided	bar chart, proc step	4
2	A Macro that can Create U.S State and U.S County KML Files	Ting Sa	This paper introduces a macro that can generate the keyhole markup language (KML) files for U.S. states and counties. The generated KML files can be used directly by Google Maps to add customized state and county layers with user-defined colors and transparencies. When someone clicks on the state and county layers in Google Maps, customized information is shown. To use the macro, the user needs to prepare only a simple SAS input data set. The paper includes all the SAS codes for the macro and provides examples that show you how to use the macro as well as how to display the KML files in Google Maps	create table, order by, dataset, proc sql	10
3	A Walk through Time: Growing Your SAS Career	Arthur L. Carpenter	The measurement of time is an integral component of most of our data sets. We note the date of the patient's visit and both the date and the time of the administration of a test. The accurate calculation of time intervals, such as the time between drug delivery and onset of an adverse event, becomes critically important. Fortunately SAS ships with a diverse set of tools for working with dates and times. As SAS programmers and as users of SAS Software, time measurement is important, not only in the successful analysis of our data, but also as a gauge in the growth of our career. To be successful we must be able to measure and work with time values, but time is also a measure of the progression of our career. This paper introduces a number of measures of time, a variety of analytic techniques applied to date and time values, interwoven with a discussion of the Grand Canyon, the Great Wall of China, and the continued growth of our professional knowledge	sysdate9, macro facility, macro variable, sas programmer	8
4	Accessibility and SAS Visual Analytics Viewers: Which Report Viewer Is Best for Your Users' Needs?	Jesse Sookne, Kristin Barker, Joe Sumpter, Lavanya Mandavilli	Many organizations that use SAS Visual Analytics must conform with accessibility requirements such as Section 508, the Americans with Disabilities Act, and the Accessibility for Ontarians with Disabilities Act. SAS Visual Analytics provides a number of different ways to view reports, including the SAS Report Viewer and SAS Mobile BI native apps for Apple iOS and Google Android. Each of these options has its own strengths and weaknesses when it comes to accessibility-a one-size-fits-all approach is unlikely to work well for the people in your audience who have disabilities. This paper provides a comprehensive assessment of the latest versions of all SAS Visual Analytics report viewers, using the Web Content Accessibility Guidelines (WCAG) version 2.0 as a benchmark to evaluate accessibility. You can use this paper to direct the end users of your reports to the viewer that best meets their individual needs	bar chart, line chart	18
5	ADQRS: Basic Principles for Building Questionnaire, Rating and Scale Analysis Datasets	Nancy Brucken, Karin LaPann	Questionnaires, ratings and scales (QRS) are frequently used as primary and secondary analysis endpoints in clinical trials. The Submission Data Standards (SDS) QRS sub-team has compiled a considerable library of SDTM supplements defining standards for the collection and storage of QRS data. The ADaM ADQRS sub-team has been formed to develop addenda to these supplements, which will define standards for corresponding analysis datasets. This paper represents the current thinking of the ADQRS sub-team regarding basic principles for building QRS analysis datasets	adam dataset, missing value, imputation, sample, cdisc	6
6	Advanced Macro: Driving a Variable Parameter System with Metadata	Arthur L. Carpenter	When faced with generating a series of reports, graphs, and charts; we will often use the macro language to simplify the process. Commonly we will write a series of generalized macros, each with the capability of creating a variety of outputs that depend the macro parameter inputs. For large projects, potentially with hundreds of outputs, controlling the macro calls can itself become difficult. The use of control files (metadata) to organize and process when a single macro is to be executed multiple times was discussed by Rosenbloom and Carpenter (2015). But those techniques only partially help us when multiple macros, each with its own set of parameters, are to be called. This paper discusses a technique that allows you to control the order of macro calls along with each macro's parameter set, while using a metadata control file	macro call, call execute, by group processing, proc import, sas log, proc step	10
7	Agricultural Trip Generation - Linking Spatial Data and Travel Demand Modeling using SAS	Alan Dybing	Data linkages from GIS and Cube Voyager require advanced knowledge of these software. The four-step travel demand modeling procedure (TDM) is commonly used to estimate and forecast traffic volumes for use in transportation planning. The trip generation step of the four-step model seeks to estimate the trip attractions and productions representing individual trips originating or terminating with a geographic boundary. Freight trip generation in rural areas within the great plains primarily result from agricultural production and marketing. This paper outlines a procedure for utilizing satellite imagery to estimate agricultural truck trip production at the township level linking ArcGIS and SAS to generate trip generation tables for use for TDM purposes	proc import, calendar year	9
8	AIR Binder 2.0: A Dynamic Visualization, Data Analysis and Reporting SAS Application for Preclinical and Clinical ADME Assays, Pharmacokinetics, Metabolite Profiling and Identification	Hao Sun, Kristen Cardinal, Richard Voorman	Although regulatory agencies request pharmacometric datasets be submitted as SAS transport files for review, current clinical and preclinical ADME data analysis is handled mostly by non-SAS programs. Previously, we reported a SAS-based application, AIR Binder, for automatic analysis and reporting of a specific cytochrome P450 (CYP) inhibition assay, a key preclinical drug metabolism assay for the prediction of drug-drug interactions (PharmaSUG 2017). Significantly improved productivity and efficiency resulted in the expansion of the application to a wide range of preclinical and clinical ADME assays for dynamic visualization, data analysis and reporting. Considering the complexity of data structures and presentation styles across these assays, SAS macros were designed and written to be more generalized and object-oriented	proc step, standard deviation, data science, proc nlin, logistic regression, sample, macro function, proc glm, anova, logistic distribution, proc reg, proc genmod, regression analysis, macro variable, numeric functions, bar chart, exposure, geomean, proc anova, proc sgpanel, normalization, predictor variable, post hoc test, call symput, proc sgplot, macro definition	26
9	An Array of Possibilities: Manipulating Longitudinal Survey Data with Arrays	Lakhpreet Gill	SAS arrays are well-suited for handling longitudinal survey data, which are data collected at multiple time points for a sampled population. Depending on the research question and selected sample, the study period for observing respondents may vary based on when they entered and exited the survey. For example, a researcher may only want to add respondents to the study when they reach a certain age, and, in contrast to a cross-sectional study, this will occur at different waves of the survey. In this paper, we will use the Health and Retirement Study to identify differing baseline and follow-up measurements conditioned on age and response status, and learn different methods for leveraging SAS arrays to manipulate the data	do loop, time series analysis, array, if-then-else, binary variable, raw data	8
10	An Introduction to ODS Statistical Graphics	Kirk Paul Lafler	Delivering timely and quality looking reports, graphs and information to management, end users, and customers is essential. This presentation provides SAS users with an introduction to `ODS` Statistical Graphics found in the Base -SAS software. Attendees learn basic concepts, features and applications of `ODS` statistical graphic procedures to create high-quality, production-ready output; an introduction to the statistical graphic `SGPLOT` procedure, `SGPANEL` procedure, and `SGSCATTER` procedure; and an illustration of plots and graphs including histograms, vertical and horizontal bar charts, scatter plots, bubble plots, vector plots, and waterfall charts	bar chart, proc sgpanel, scatter plot, histogram, proc sgplot, box plot, vertical bar chart, proc freq, class variable	13
11	An Introduction to PROC REPORT	Kirk Paul Lafler	SAS users often need to create and deliver quality custom reports and specialized output for management, end users, and customers. The SAS System provides users with the `REPORT` procedure, a "canned" Base-SAS procedure, for producing quick and formatted detail and summary results. This presentation is designed for users who have no formal experience working with the `REPORT` procedure. Attendees learn the basic `PROC REPORT` syntax using the COLUMN, DEFINE, other optional statements, and `PROCEDURE OPTIONS` to produc e quality output; explore basic syntax to produce basic reports; compute subtotals and totals at the end of a report using a COMPUTE Block; calculate percentages; produce statistics for an analysis variable; apply conditional logic to control summary outpu t rows; and enhance the appearance of output results with basic Output Delivery System (`ODS`) techniques	proc report, proc step, title statement	17
12	Arbovirus, Varicella and More: Using SAS for Reconciliation of Disease Counts	Misty Johnson	upon reporting done by public health jurisdictions of the United States. Communicable disease reporting is facilitated by an electronic surveillance system that communicates directly with the CDC for specified diseases. Disease reports are generated when a disease is classified as "confirmed" or "probable." These reports can then be updated with more information during case investigation, or may be determined to not be a case. Each addition of information results in a new report generated and sent to the CDC. The receiving system at the CDC is programmed to keep only the most recent report for each case. The State of Wisconsin reconciles disease reporting between cases acknowledged by the program epidemiologist and disease reports sent to the CDC by the surveillance system on an annual basis. A SAS 9.4 program utilizing simple DATA steps with BY-group processing is used to determine which reports were counted by the CDC	macro variable, proc sql, index, proc freq, data cleaning, proc tabulate, excel, proc step, by group processing, report year, options statement	8
13	Automated Validation of Complex Clinical Trials Made Easy	Richann J. Watson, Joshua M. Horstman	Validation of analysis datasets and statistical outputs (tables, listings, and figures) for clinical trials is frequently performed by double programming. Part of the validation process involves comparing the results of the two programming efforts. `COMPARE` procedure output must be carefully reviewed for various problems, some of which can be fairly subtle. In addition, the program logs must be scanned for various errors, warnings, notes, and other information that might render the results suspect. All of this must be performed repeatedly each time the data is refreshed or a specification is changed. In this paper, we describe a complete, end-to-end, automated approach to the entire process that can improve both efficiency and effectiveness	proc compare, proc step, dataset, macro variable, data cleaning, sas output, sample, where statement, independent qc, proc report, macro parameter, macro call, macro definition	35
14	Base SAS and SAS Enterprise Guide ~ Automate Your SAS World with Dynamic Code; Your Newest BFF (Best Friend Forever) in SAS	Kent Phelps, Ronda Phelps	Communication is the basic foundation of all relationships including our SAS relationship with the Server, PC, or Mainframe. To communicate more efficiently ~ and to increasingly automate your SAS World ~ you will want to learn how to transform Static Code into Dynamic Code that automatically recreates the Static Code, and then executes the recreated Static Code automatically. Our Hands-On Workshop highlights the powerful partnership which occurs when Dynamic Code is creatively combined with a Dynamic `FILENAME` Statement, Macro Variables, the INDSNAME SET Option, and the CALL EXECUTE Command within 1 SAS Enterprise Guide Base SAS Program Node. You will have the exciting opportunity to learn how 1,469 time-consuming Manual Steps are amazingly replaced with only 1 time-saving Dynamic Automated Step. We invite you to attend our session where we will detail the UNIX syntax for our project example and introduce you to your newest BFF (Best Friend Forever) in SAS	filename statement, call execute, sas base, sas enterprise guide	35
15	Before You Get Started: A Macro Language Preview in Three Parts	Arthur L. Carpenter	Using the macro language adds a layer of complexity to SAS programming that many programmers are reluctant to tackle. The macro language is not intuitive, and some of its syntax and logic runs counter to similar operations in the `DATA STEP`. This makes the transfer of `DATA STEP` and `PROC STEP` knowledge difficult when first learning the macro language. So why should one make the effort to learn a complex counterintuitive language? Before you start to learn the syntax; where to put the semicolon, and how to use the ampersand and percent sign; you need to have a basic understanding of why you want to learn the language in the first place. It will also help if you know a bit about how the language thinks. This overview provides the background that will enable you to understand the way that the macro language operates. This will allow you to avoid some of the common mistakes made by novice macro language programmers	macro facility, macro variable, proc step, macro definition	9
16	Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS Code	Joshua M. Horstman	Nearly every SAS program includes logic that causes certain code to be executed only when specific conditions are met. This is commonly done using the IF…THEN…ELSE syntax. In this paper, we will explore various ways to construct conditional SAS logic, including some that may provide advantages over the IF statement. Topics will include the SELECT statement, the `IFC` and `IFN` functions, the CHOOSE and WHICH families of functions, as well as some more esoteric methods. We'll also make sure we understand the difference between a regular IF and the %IF macro statement	if-then-else, subsetting if, where statement, ifn, select, ifc, macro definition, sas programmer, %if-%then-%else, missing value, proc format, whichn, execution phase, macro parameter, when, proc step, macro facility	10
17	Claim Analytics	Mei Najim	In the Property & Casualty Insurance industry, advanced analytics has increasingly penetrated into each of the core business operations - marketing, underwriting, risk management, actuarial pricing, and actuarial reserving, etc. Advanced analytics has been extensively used in claims areas to predict the claims' future for the purpose of having early intervention to improve claim outcomes and claim processing efficiency. This is another important way to grow revenue and increase profit in insurance industry. This paper will first introduce several current common claim predictive models in predictive analytics. Then, a claim analytics standard modeling process will be introduced. Finally, a Worker's Compensation litigation propensity model will be introduced as a case study	data mining, data cleaning, feature selection, response variable, proc hpsplit, regression analysis, logistic regression, model validation, sas enterprise guide, insurance, incurred loss, neural network, proc genmod, logistic distribution, sas base, insurance claims, model testing, univariate analysis, validation set, feature engineering, significance level, casualty insurance, insurer, model training, predictive analytics, stepwise selection, data transformation, gradient boosting, claim severity, training set, correlation, imputation	18
18	Cleaning Messy Data: SAS Techniques to Homogenize Tax Payment Data	Aaron Barker	One challenge frequently encountered by analysts aggregating over large volumes of data is dealing with inconsistencies among data sources. Fortunately, all versions of SAS have a wide array of tools available that a user of any skill level can employ to remedy such problems. This paper uses example tax payment data to demonstrate tools which have been employed to do a novel analysis of citizens' experience with the tax system in Iowa. The first method highlighted is conditional logic on identifier variables to determine how best to interpret the data in the identifier column. This is shown both using IF THEN logic and then again using SELECT and WHEN statements. The second method is determining when identifiers are of different types (e.g. permit numbers vs. Social Security Numbers) and then bringing in external data sources to recode inconsistent identifiers into consistent ones. This is shown using both `PROC SQL` as well as the traditional `MERGE` statement	unique key, proc sql, proc step, if-then-else, credit card, when, merge statement, data cleaning, proc freq	8
19	Code Like It Matters: Writing Code That's Readable and Shareable	Paul Kaefer	Coming from a background in computer programming to the world of SAS yields interesting insights and revelations. There are many SAS programmers who are consultants or work individually, sometimes as the sole maintainer of their code. Since SAS code is designed for tasks like data processing and analytics, SAS developers working on teams may use different strategies for collaboration than those used in traditional software engineering. Whether a programmer works individually, on a team, or on a project basis (delivering code and moving on to the next project), there are a number of best practices from traditional soft ware engineering that can be leveraged to improve SAS code. These practices make it easier to read, maintain, and understand/remember why the code is written the way it is. This paper presents a number of best practices, with examples and suggestions for u sage in SAS	sas enterprise guide, macro function, macro variable, proc step, sas programmer, proc sql, macro definition, sample	11
20	Computing Risk Measures for Loan Facilities with Multiple Lines of Draws	Chaoxian Cai	In commercial lending, a commitment facility may have multiple lines of draws with hier archical loan structures. The risk measures for main, limit, and sublimit commitments are usually aggregated and reported at the main obligation level. Thus finding all hierarchical loan structures in loan and commitment tables is required in order to aggregate the risk meas ures. In this paper, I will give a brief introduction of commercial loans from simple standalone loan s to revolving and non -revolving commitments with complex loan structures. I will present a SAS macro program, which can be used to identify main obligations and loan s tructures of future commitments from loan and commitment relational tables using Base SAS DATA steps. Risk measures, such as exposure at default (EAD) and credit conversion factor (CCF), are computed for these complicated loans and illustrated by examples	dataset, credit conversion factor, ead, commercial loan, macro function, letters of credit, sas base, retail banking, macro definition, proc sql	10
21	Correcting for Selection Bias in a Clinical Trial	Shana Kelly	Selection bias occurs when data does not represent the population intended, and balance across all potential confounding factors based on randomization did not happen. Selection bias can cause misleading results when doing statistical analysis, and should be corrected for. This paper ex plores a few alternative techniques to correct for a disparity between the various comparison groups in a clinical trial. Food Prescription is a small clinical trial conducted by Spectrum Health to get impoverished individuals in the Grand Rapids community with a chronic disease such as diabetes to consume more fresh fruits and vegetables. Health outcomes are compared between the treatment and control groups after taking into account all covariates. The procedures shown are produced using SAS Enterprise Gu ide 7.1	clinical trial, estimate, binary variable, logistic regression, proc glmselect, likelihood function, logistic distribution, proc step, maximum likelihood estimation, response variable, anova	8
22	Correlation and Structural Equation Analysis on the Effects of Anti-Discrimination Policies and Resources on the Well Being of Lesbian, Gay, and Bisexual College Students	Brandy R. Sinco, Michael Woodford, Jun Sung Hong, Jill Chonody	Methods: Among a convenience sample of cisgender LGBQ college students (n=268), we examined the association between college- and state-level structural factors and students' experiences of campus hostility and microaggressions, psychological distress, and self-acceptance. Relationships between these outcomes were first examined with Spearman correlation coefficients. Structural Equation Modeling (SEM) was used to explore the meditating relationship of college-level structural factors on discrimination, distress, and self-acceptance. SAS `PROC CORR` was used for the correlation analysis and `PROC CALIS` was used for the SEM. The EffPart feature in `PROC CALIS` was used to test for a mediating effect from an inclusive non- discrimination policy to (hostility and microaggressions) to psychological distress. Results: State-level factors were not correlated with students' experiences nor psychological well being	correlation, proc step, proc calis, covariance, sample, proc reg, spearman correlation, degrees of freedom, maximum likelihood estimation, likelihood function, goodness of fit, regression analysis, proc corr, output delivery system	22
23	Data Can Be Beautiful: Crafting a Compelling Story with SAS Visual Analytics	Cheryl L. Coyle	Do your reports effectively communicate your message? Are your reports aesthetically pleasing? This paper provides guidance for SAS Visual Analytics Designer users to create compelling data stories. The primary goal of a report is to enable readers to quickly and easily get answers to their questions. The visualizations you use for your data, the quantity and arrangement of information included, and the use of color all contribute to the reader's ability to understand your report. This paper describes how to guide your reader's movement through a report to support comprehension of the data story. It includes tips on how to express quantitative data using the most appropriate graphs, suggests ways to organize content using visual design techniques, and explains how color can be used to enhance the efficacy of a report. A thoughtfully designed report can educate the viewer without compromising visual appeal	bar chart, data visualization, missing value, line chart, scatter plot, series plot	20
24	Data Quality Control: Using High Performance Binning to Prevent Information Loss	Lakshmi Nirmala Bavirisetty, Deanna Naomi Schreiber-Gregory	It is a well-known fact that the structure of real-world data is rarely complete and straight-forward. Keeping this in mind, we must also note that the quality, assumptions, and base state of the data we are working with has a very strong influence on the selection and structure of the statistical model chosen for analysis and/or data maintenance. If the structure and assumptions of the raw data are altered too much, then the integrity of the results as a whole are grossly compromised. The purpose of this paper is to provide programmers with a simple technique which will allow the aggregation of data without losing information. This technique will also check for the quality of binned categories in order to improve the performance of statistical modeling techniques. The SAS high performance analytics procedure, HPBIN, gives us a basic idea of syntax as well as various methods, tips, and details on how to bin variables into comprehensible categories	proc step, interval variable	9
25	DATA Step in SAS Viya: Essential New Features	Jason Secosky	The `DATA STEP` is the familiar and powerful data processing language in SAS and now SAS Viya. The `DATA STEP`'s simple syntax provides row-at-a-time operations to edit, restructure, and combine data. New to the `DATA STEP` in SAS Viya are a varying-length character data type and parallel execution. Varying- length character data enables intuitive string operations that go beyond the 32KB limit of current `DATA STEP` operations. Parallel execution speeds the processing of big data by starting the `DATA STEP` on multiple machines and dividing data processing among threads on these machines. To avoid multi - threaded programming errors, the run-time environment for the `DATA STEP` is presented along with potential programming pitfalls. Come see how the `DATA STEP` in SAS Viya makes your data processing simpler and faster	by group processing, cpu time, sas viya, character variable, proc step, big data, data wrangling, parallel processing, character functions	14
26	Demystifying Intervals	Derek P. Morgan	Intervals have been a feature of base SAS for a long time, allowing SAS users to work with commonly (and not-so-commonly) defined periods of time such as years, months, and quarters. With the release of SAS 9, there are more options and capabilities for intervals and their functions. This paper will first discuss the basics of intervals in detail, and then we will discuss several of the enhancements to the interval feature, such as the ability to select how the INTCK() function defines interval boundaries and the ability to create your own custom intervals beyond multipliers and shift operators	sysdate9, missing value, sample, date variable, time series analysis	15
27	Dimensionality Reduction using Hadamard, Discrete Cosine and Discrete Fourier Transforms in SAS	Mohsen Asghari, Aliasghar Shahrjooihaghighi, Ahmad Desoky	Dimensionality reduction studies various techniques to transform data in the most compact and efficient manner that allows modeling, analyzing, and predicting information with insignificant errors. Principle component analysis (PCA) is a method for reducing the dimensionality by decreasing the number of variables and selecting a smaller subset of uncorrelated transformed variables called principal components. PCA is data dependent and requires the computation of the correlation matrix of input data as well as performs Singular Value Decomposition (SVD) of that matrix. Hadamard, Discrete Cosine Transform (DCT), and Discrete Fourier Transform (DFT) are orthogonal transformations that are not data dependent and reduce the dimensionality by decreasing the correlation of the transform components. In this paper, we implemented Hadamard, DCT, and DFT in SAS on a standard dataset. Also, we compared the results of these transformations and PCA technique	proc princomp, scree plot, dimensionality reduction, proc step, dataset, correlation	12
28	DOSUBL and the Function Style Macro	John Henry King	The introduction of the SAS function DOSUBL has made it possible to write certain function style macros that were previously impossible or extremely difficult. This Beyond the Basics talk will discuss how to write a function style macro that uses DOSUBL to run SAS code and return an "Expanded Variable List" as a text string. While this talk is directed toward a specific application the techniques can be more generally applied	macro variable, proc transpose, proc step, variable list, variable, proc sql, select	6
29	Exploring HASH Tables vs. SORT/DATA Step vs. PROC SQL	Lynn Mullins, Richann J. Watson	There are often times when programmers need to merge multiple SAS data sets to combine data into one single source data set. Like many other processes, there are various techniques to accomplish this using SAS software. The most efficient method to use based on varying assumptions will be explored in this paper. We will describe the differences, advantages and disadvantages, and display benchmarks of using HASH tables, the SORT and `DATA STEP` procedures, and the `SQL` procedure	hash table, proc sql, proc step, lookup	14
30	Exploring the Relationship Between Substance Abuse and Dependence Disorders and Discharge Status: Results and Implications	Deanna Naomi Schreiber-Gregory	The goal of this study was to investigate the association between substance abuse and dependence diagnoses and discharge status for patients admitted to a short-term acute care facility in the United States while controlling for gender, age, marital status, region, admission type, primary form of payment, days of care, and race. A series of univariate and multivariate logistic regression analyses as well as a propensity analysis were conducted via SAS9.4 and replicated through SAS Studio to explore the association of the target var iable, a primary diagnosis of substance abuse or dependence, with the treatment variable, discharge status, and identified control variables among patients who were admitted to a short -term acute care facility in the United States. The results revealed a s ignificant relationship between having a primary substance abuse or dependence diagnosis and discharge status while controlling for discharge status propensity and possible confounding variables	odds ratio, propensity score, logistic regression, regression analysis, logistic distribution, sample, p-value, population, response variable	25
31	Extraction and Use of Text Strings with SAS when Source exceeds the 32k String Length Limit	John Schmitz	Database Systems support text fields that can be much larger than what are supported by SAS and `SAS/ACCESS` software. These fields may contain notes, unparsed and unformatted text, or XML data. This paper offers a solution for lengthy text data. Using SQL explicit pass-through, minimal native SQL coding, and some SAS macro logic, SAS developers can easily extract these strings as a set of substring elements that SAS can process and store without data loss	proc sql, sql pass-through, macro variable, proc step, do loop, macro call, macro definition, dataset, character variable, macro facility, select	10
32	Fifteen Functions to Supercharge Your SAS Code	Joshua M. Horstman	The number of functions included in SAS software has exploded in recent versions, but many of the most amazing and useful functions remain relatively unknown. This paper will discuss such functions and provide examples of their use. Both new and experienced SAS programmers should find something new to add to their toolboxes	anyalpha, if-then-else, character functions, tranwrd function, notalpha, sas programmer, translate, whichn, missing value	9
33	From Device Text Data to a Quality Data Set	Laurie Smith	Data quality in research is important. It may be necessary to obtain d ata from a device to be used a research. Often it is read from an external text file and entered onto a CRF. The data is then read from the CRF and entered into a database. This `PROC` ess introduces many opportunities for data quality to be compromised. The quality of device data used in a study can be greatly improved if the data can be read directly from a device's output file into a dataset. If the device outputs results into a tex t file that can be saved electronically, SAS can be used to read the data needed from the results and saved into a dataset. In addition to improving data quality, data collection and monitoring time can also be reduced by taking advantage of these electronic files as opposed to recapturing this data on a CRF	txt, data step, character variable, data cleaning	6
34	Fully Automated Updating of Arbitrarily Complex Excel Workbooks	David Oesper	You can generate some very sophisticated `EXCEL` workbooks using `ODS` `EXCEL` and `ODS` `TAGSETS`.EXCELXP, but sometimes you'll want to create your `EXCEL` workbook in Microsoft `EXCEL` or someone else will provide it to you. I'll show you can use SAS to dynamically update (and distribute) any existing `EXCEL` workbook with no manual intervention required. You'll need only Base SAS 9.4 TS1M2 or later and `SAS/ACCESS` to PC Files to use this approach. Examples in both the Linux and Windows operating environments will be presented	xls, excel, macro variable, dataset	4
35	Generating Reliable Population Rates Using SAS Software	Jack N. Shoemaker	The business of health insurance has always been to manage medical costs so that they don't exceed premium revenue. Monitoring and knowing about these patient populations will mean the difference between success and financial ruin. At the core of this monitoring are population rates like per member per month costs and utilization per thousand. This paper describes techniques using SAS software that will genera te these population rates for an arbitrary set of population dimensions. Keeping the denominators in sync with the numerators is key for implementing trustworthy drill - down applications involving population rates	proc step, macro variable	6
36	Get Smart! Eliminate Kaos and Stay in Control - Creating a Complex Directory Structure with the DLCREATEDIR Statement, SAS Macro Language, and Control Tables	Louise S. Hadden	An organized directory structure is an essential cornerstone of data analytic development. Those programmers who are involved in repetitive processing of any sort control their software and data quality with directory structures that can be easily replicated for different time periods, different drug trials, etc. Practitioners (including the author) often use folder and subfolder templates or shells to create identical complex folder structures for new date spans of data or projects, or use manual processing or external code submitted from within a SAS process to run a series of MKDIR and CHDIR commands from a command prompt to create logical folders. Desired changes have to be made manually, offering opportunities for human error. Since the advent of the DLCREATEDIR system option in SAS version 9.3, practitioners can create single folders if they do not exist from within a SAS process	proc sql, macro definition	9
37	GREMOVE, Reassign, and let's GMAP! A SAS Trick for Generating Contiguous Map Boundaries for Market-Level Research	Chad Cogan, Jeffrey Pearson, Purna Mukhopadhyay, Charles Gaber, Marc Turenne	In health services research, accurate health care market definitions are crucial for assessing the potential market-level consequences of policy changes. Political units of geography (e.g. counties) are generally not sufficient for capturing the service area of a provider. Alternatively, researchers may generate customized boundaries using data-driven approaches based on patient flow only to find that their newly defined areas are not contiguous. We use a novel approach to correct for the lack of contiguity using the information produced by the `GREMOVE` procedure. GREMOVE is often used along with the GMA P procedure when there is a need to generate customized boundaries on a map by removing the internal boundaries of smaller units of geography. However, SAS users may not be aware of the logic used by `PROC GREMOVE` to assign segment values and the underlying data that goes into the maps. We first examine the logic used by `PROC GREMOVE`, and the map output data set it produces	proc step, proc gmap	19
38	Hands-on Introduction to SAS and the ODS Excel Destination	Kirk Paul Lafler	SAS software is the "gold" standard for robust and reliable data access, manipulation, analytics, analysis, reporting, and data discovery. Microsoft `EXCEL` is the most widely used software in the world. This hands-on workshop (HOW) introduces the Output Delivery System (`ODS`) `EXCEL` destination syntax; along with the application and construction of SAS and `ODS` `EXCEL` code to share data, tables, statistics, images, reports, and results to create exciting and robust Microsoft `EXCEL` files and workbooks. Keywords SAS, `ODS`, `ODS` `EXCEL`, `EXCEL` workbooks, styles, images	excel, proc step, dataset, proc export, proc univariate, proc import, output delivery system, proc print, ods html, sas output, libname statement, bar chart, libname engine, ods pdf, proc template, proc sql	26
39	How Can an NBA Player Be Clutch?: A Logistic Regression Analysis	Logan Edmonds	Many NBA players are known as clutch shooters that put fear in the opposing team as the clock is winding down. Michael Jordan is remembered as much for his game ending shots as the high -flying dunks. We know that these players can make the shot, but are there certain situations that contribute to the likelihood the game winner will go in? Using `PROC LOGISTIC`, this paper determines the key components of made shots during the crucial last two minutes of an NBA game. Using shot log data from the 2014-2015 NBA season, over 120,000 shots were filtered to those occurring in the last two minutes of regulation or overtime. Many things can affect whether a player will make these high pressure shots. Specifically, the effects of home court, back-to-back game fatigue, shot distance and dribble time before the shot are considered as possible predictors of clutch shooting	stepwise selection, logistic distribution, forward selection, logistic regression, proc logistic, proc step, regression analysis, predictor variable, correlation	18
40	If You Build It, Will They Understand? Designing Reports for the General Public in SAS Visual Analytics	Jesse Sookne, Ed Summers, Julianna Langston, Karen Mobley	Your organization already uses SAS Visual Analytics and you have designed reports for internal use. Now you want to publish a report on your external website. How do you design a report for the general public considering the wide range of education and abilities? This paper defines best practices for designing reports that are universally accessible to the broadest audience. You learn tips and techniques for designing reports that the general public can easily understand and use to gain insight. You also learn how to leverage features that help you comply with your legal obligations regarding people with disabilities. The paper includes recommendations and examples that you can apply to your own reports	bar chart, line chart, time series analysis, scatter plot, series plot	20
41	Ignorance is not bliss - understanding SAS applications and product contents	Jayanth Iyengar	Have you ever heard 'SAS Display Manager' in a presentation or discussion, and wondered to yourself, what exactly is SAS Display Manager? Or have you come across '`SAS/SQL`' or '`SAS/MACROS`' in a job description and thought that SQL or Macros are separate package modules of the SAS System. There's a fair amount of confusion and misinformation regarding SAS products, and what they're composed of, even amongst experienced SAS users. In this paper, I attempt to provide a proper understanding of SAS components, and distinguish between SAS applications and SAS modules. Also, I show how to determine what products are licensed and installed in the SAS windowing environment	sas base, proc step, sas enterprise guide, sas studio, sas log, proc sql, relational database	7
42	Intermediate SAS ODS Graphics	Chuck Kincaid	This paper will build on the knowledge gained in the Intro to SAS `ODS` Graphics. The capabilities in `ODS` Graphics grow with every rele ase as both new paradigms and smaller tweaks are introduced. After talking with the `ODS` developers, a selection of the many wonderful capabilities was selected. This paper will look at that selection of both types of capabilities and provide the reader with more tools for their belt. Visualization of data is an important part of telling the story seen in the data. And while the standards and defaults in `ODS` Graphics are very well done, sometimes the user has specific nuances for characters in the story or additional plot lines they want to incorporate. Almost any possibility, from drama to comedy to mystery, is available in `ODS` Graphics if you know how. We will explore tables, annotation and changing attributes, as well as the BLOCK plot. Any user of Base SAS on any platform will find great value from the SAS `ODS` Graphics procedures	proc step, proc sgplot, bar chart, scatter plot, output delivery system, nominal variable, line chart	28
43	ISO 8601 and SAS: A Practical Approach	Derek P. Morgan	The ISO 8601 standard for dates and times has long been adopted by regulatory agencies around the world for clinical data. While there are many homemade solutions for working in this standard, SAS has many built-in solutions, from formats and informats that even take care of time zone specification, to the IS8601_CONVERT routine, which painlessly handles durations and intervals. These built-in capabilities, available in SAS 9.2 and above, will streamline your code and improve efficiency and accuracy. This paper also assumes the use of SAS version 9.2 and above	datetime, imputation, sysdate9, character variable	9
44	Lag Models with Social Response Outcomes	David J. Corliss	Lag models are a type of time series analysis where the current value of an outcome variable is modeled based, at least in part, on previous values of predictor variables. This creates new opportunities for the use of social media data, both as the result of previous events and as predictors of future outcomes. This paper demonstrates lag models with social media data to establish a connection between severe solar storms and subsequent hardware failures based on complaints recorded in Twitter. The methodology is then used to investigate the possibility of a statistical link between hate speech and subsequent acts of violence against persons targeted by the speech. These Data For Good analyses have been performed using SAS University Edition, a free version of SAS available to students, professors and non-profit researchers	time series analysis, exploratory data analysis	9
45	Let's Get FREQy with our Statistics: Let SAS Determine the Appropriate Test Statistic Based on Your Data	Lynn Mullins, Richann J. Watson	As programmers, we are often asked to program statistical analysis procedures to run against the data. Sometimes the specifications we are given by the statisticians will outline which statistical procedures to run. But other times, the statistical procedures to use need to be data dependent . To run these procedures based on the results of previous procedures' output requires a little more preplanning and programming. We will present a macro that will dynamically determine which statistical procedure to run based on previous procedure output, as well as, allowing the user to input parameters (for example, fshchi, plttwo, catrnd, bimain, and bicomp) and the macro will return counts, percents and appropriate p-value for Chi vs. Fisher and p-value for Trend and Binomial CI, if applicable	macro parameter, proc freq, binomial distribution, proc step, sample, sas output, class variable	14
46	Logistic Model Selection with SAS PROC's LOGISTIC, HPLOGISTIC, HPGENSELECT	Bruce S. Lund	In marketing or credit risk a model with binary target is often fitted by logistic regression. In this setting the sample size is large and the model includes many predictors. For these models this paper discusses the variable selection procedures that are offered by `PROC LOGISTIC`, `PROC HPLOGISTIC`, and `PROC HPGENSELECT`. These selection procedures include the best subsets approach of `PROC LOGISTIC`, selection by best SBC of `PROC HPLOGISTIC`, and selection by LASSO of `PROC HPGENSELECT`. The use of classification variables in connection with these selection procedures is discuss ed. Simulations are run to compare these methods	proc step, logistic distribution, proc logistic, logistic regression, proc glmselect, likelihood function, regression analysis, sample, proc hplogistic, degrees of freedom, predictor variable, proc hpgenselect, sample size, forward selection, data cleaning, significance level, binary variable, maximum likelihood estimation, validation set, feature selection	18
47	Macro that can Provide More Information for your Character Variables	Ting Sa	Sometimes, we want to change character variables to numerical variables in batches. But before doing that, we may need to manually check if those variables contain only numerical values. Also sometimes, we want to make all the date or `DATETIME` formats be consistent across the SAS data sets, but if those variables are saved as character variables and we don't have a data dictionary, we will have to manually check the data sets to see their formats. Besides, we may have those character variables that only contain blank values and we can delete them to save space. Using the macro in this paper, you can get those information for each character variable without manually checking one by one. The macro can check all the character variables in a library or in some data sets. An `EXCEL` report will be generated after running the macro to include the information about each checked character variable. Users can use the `EXCEL` report to further filter the information	character variable, date variable, excel, missing value	7
48	Make That Report Look Great Using the Versatile PROC TABULATE	Ben Cochran	Several years ago, one of my clients was in the business of selling reports to hospitals. He used `PROC TABULATE` to generate part of these reports. He liked the way this procedure 'crunched the numbers', but not the way the final reports looked. He said he would go broke if he had to sell default `PROC TABULATE` output. So, he wrote his own routine to take TABULATE output and render it through Crystal Reports. This was in during the days of the 6.12 release of SAS before there was something called the Output Delivery System (`ODS`). Once he got his hands on SAS `ODS`, he kissed his Crystal Reports good-bye. This paper is all about using `PROC TABULATE` to generate fantastic looking reports. If you want to generate BIG money reports with `PROC TABULATE`, this presentation is for you	proc tabulate, proc step, output delivery system, class variable	12
49	Mapping MRI data to SDTM and ADaM	Lingling Xie, Xiaoqi Li	Magnetic Resonance Imaging ( MRI) has been used to detect structure damage in spine and sacroiliac joints (SIJ) in clinical trials. The complexity in the data includes multiple time points, multiple methods, high dimensionality and adjudicated readings. The raw data contains information on scoring method, test-type, location, region of interest/feature, laterality and slice, which introduces more than 600 records from each reader at each time point and challenging work for the SDTM and ADaM . For SDTM, we manage to map the data to XP domain. For ADaM, we map it to a Basic Data Structure analysis dataset that contains more than 600 parameters combining information in all dimensions, with additional five parameters for the derived total scores based on the complicated scoring algorithms including how to handle missing data and consolidation of the scores from different readers	do loop, missing value, controlled terminology, adam dataset, cdisc	10
50	Merge with Caution: How to Avoid Common Problems when Combining SAS Datasets	Joshua M. Horstman	Although merging is one of the most frequently performed operations when manipulating SAS datasets, there are many problems which can occur, some of which can be rather subtle. This paper examines several common issues, provides examples to illustrate what can go wrong and why, and discusses best practices to avoid unintended consequences when merging	merge statement, by group processing, out=, in=, dataset, pdv, length statement, sas log, proc sql	14
51	Multiple Imputation of Family Income Data in the 2015 Behavioral Risk Factor Surveillance System	Jia Li, Aaron Sussell	Multiple imputation methods are increasingly used to handle missing data in statistical analyses of observational studies to reduce bias and improve precision. `SAS/STAT` `PROC MI` can be used t o impute continuous or categorical variables with a monotone or arbitrary missing pattern. This study used the fully conditional specification (FCS) method to impute the family income variable in the 2015 Behavioral Risk Factor Surveillance System (BRFSS) data. BRFSS is a state-based health survey that collects data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. In this paper, the study population was restricted to currently emp loyed respondents (age>=18) from the 25 states that collected industry and occupation information. Of the total 87,483 respondents, 11% were missing income information	imputation, sample, logistic regression, missing value, logistic distribution, regression analysis, proc mi, multiple imputation, proc step, nominal variable	4
52	No News Is Good News: A Smart Way to Impute Missing Clinical Trial Lab Data	Ming Yan	In clinical trials, specific laboratory microscopic UA, RBC morphology, and special WBC subordinate tests are reported to sponsors ONLY if an abnormality is observed. The normal results which are not explicitly reported, however, need to be in place in order to compute a percentage of abnormal ities for each lab test in the subject population. This macro starts from SDTM LB domain that only stores observed abnormal lab tests and converts it to ADaM with all the normal test results filled out for all patients at all time-point	clinical trial, sample, cdisc	4
53	Nothing to SNF At: Evaluating an intervention to reduce skilled nursing home (SNF) length of stay	Andrea Frazier	Length of stay (LOS) in skilled nursing facilities (SNF) is a driver of high health care costs, particularly form Medicare patients. This study used survival analysis techniques to examine the effect of a simple intervention (educating providers and case managers on expected LOS for each patient , as determined by national benchmarks) on SNF LOS. This paper also considers techniques used to abet particular data collection challenges in the study	case management, survival analysis, insurance claims, regression analysis, proc phreg	4
54	Oscars 2017 - Text Mining & Sentimental Analysis	Karthik Sripathi	It has always been fascinating to realize how the magnitude of award shows have been increasing year after year. It is the enormously positive response of audience that keeps the stage shows to envisage. We know that sentiments of people play a crucial role in deciding the prospects of a particular event. This paper summarizes the sentiments of individuals towards one of the most awards popular show, Oscars. It provides crucial insights on how people sentiments could determine the success or failure of a sh ow. The paper involves text mining of people's reactions towards the 2017 Oscars in general and a sentiment analysis of regarding the best picture mix up using SAS Sentiment Analysis Studio. This paper aims to determine the success of an awards show based on individual sentiments before the show, during and after the show. This information will give a better picture of how to handle any unwanted circumstances during the event	proc freq, normalization	9
55	Parsing Useful Data Out of Unusual Formats Using SAS	Andrew T. Kuligowski	Most "Introduction to Programming" courses will includ e a section on reading external data; the first assumption they make will be that the data are stored in some sort of documented and consistent format. Fortunately, in the "real world", a lot of the data we deal with has the same basic assumption of occurring in a documented, consistent format - a lot of it, but not all of it. This presentation will address some techniques that ca n be used when we are not dealing with cleanly formatted data, when the data we want is in a less-t han-ideal format, perhaps intermingled or seemingly buried with unnecessary clutter. It will discuss the principles of using SAS to parse a file to extract useful data from a normally unusable source. This will be accomplished by citing examples of unusual data sources and the SAS Code used to parse it Keywords: INDEX Function, INPUT Statem ent, Parser	input statement, named input, proc print, sample, proc step, conditional do loop, cpu time, index, sas log, sas base, character functions, end	15
56	Patient Safety with SAS Visual Analytics	Piyush K. Singh, Prasoon Sangwan, Ghiyasuddin Mohammed Faraz Khan	In clinical trials, ensuring patient safety is the supreme priority. Stringent monitoring of safety outcomes is a key regulatory consideration. A trial may coincide with unacceptable patient safety within a given indication and needs to be captured as early as possible to save time and money. Monitoring is done by generating various reports to identify any adverse event, checking its severity, identifying its reason etc. These reports can be huge and may take a lot of time and effort to put them together to come to an outcome. In this paper we will explain how SAS Visual Analytics can play a vital role in generation of reports to enable easy and deep down monitoring of the patient safety. Its features not only helps in quick generation of reports but also help the monitoring committees to answer their queries , draw inferen ces and give a complete view of the safety outcome of the trial	adverse events, bar chart, clinical trial, system organ class, serious adverse event, sample, sas visual analytics, proc freq	12
57	PROC DOC III: Self-generating Codebooks Using SAS	Louise S. Hadden	This paper will demonstrate how to use good documentation practices and SAS to easily produce attractive, camera-ready data codebooks (and accompanying materials such as label statements, format assignment statements, etc.) Four primary steps in the codebook production process will be explored: use of SAS metadata to produce a master documentation spreadsheet for a file; review and modification of the master documentation spreadsheet; import and manipulation of the metadata in the master documentation spreadsheet to self-generate code to be included to generate a codebook; and use of the documentation metadata to self-generate other helpful code such as label statements. Full code for the example shown (using the SASHELP.HEART data base) will be provided upon request	proc step, proc export	8
58	Proc Transpose Cookbook	Douglas Zirbel	`PROC TRANSPOSE` rearranges columns and rows of SAS datasets, but its documentation and behavior can be difficult to comprehend. For common input files, this paper will show a variety of desired output files, plus code and explanations	proc step, proc transpose, dataset, by group processing, lookup	12
59	Quotes within Quotes: When Single (') and Double (") Quotes are not Enough	Arthur L. Carpenter	Although it does not happen every day, it is not unusual to need to place a quoted string within another quoted string. Fortunately SAS recognizes both single and double quote marks and either can be used within the other. This gives us the ability to have two deep quoting. There are situations, however where two kinds of quot es are not enough. Sometimes we need a third layer or more commonly we need to use a macro variable within the layers of quotes. Macro variables can be especially problematic as they will generally not resolve when they are inside single quotes. However this is SAS, and that implies that there are several things going on at once and that there are several ways to solve these types of quoting problems. The primary goal of this paper is to assist the programmer with solutions to the quotes within quotes problem with special emphasis on the presence of macro variables	macro variable, filename statement, macro function, macro facility, &variable, macro quoting function, macro put, proc step	7
60	Red Rover, Red Rover, Send Data Right Over: Exploring External Geographic Data Sources with SAS	Louise S. Hadden	The intrepid Mars Rovers have inspired awe and Curiousity - and dreams of mapping Mars using `SAS/GRAPH`. This presentation will demonstrate how to import SHP file data (using `PROC MAPIMPORT`) from sources other than SAS and GfK to produce useful (and sometimes creative) maps. Examples will include mapping neighborhoods, ZCTA5 areas, postal codes and of course, Mars. Products used are Base SAS and `SAS/GRAPH`. SAS programmers of any skill level will benefit from this presentation	proc step, proc format	9
61	SAS/GRAPH and GfK Maps: a Subject Matter Expert Winning Combination	Louise S. Hadden	SAS has an amazing arsenal of tools to use and display geographic information that is relatively unknown and underutilized. High quality GfK Geocoding maps have been provided by SAS since SAS 9.3 M2, as sources for inexpensive map data dried up. SAS has been including both GfK and "traditional" SAS map data sets with licenses for `SAS/GRAPH` for some time, recognizing there will need to be an extended transitional period. However, for those of us who have been putting off converting our `SAS/GRAPH` mapping programs to use the new GfK maps, the time has come, as the "traditional" SAS map data sets are no longer being updated. If you visit SAS MapsOnline, you will find only GfK maps in current maps. The GfK maps are updated once a year. This presentation will walk through the conversion of a long-standing SAS program to produce multiple US maps for a data compendium to take advantage of GfK maps, as well as several other examples. Products used are Base SAS and `SAS/GRAPH`	proc step, create table	22
62	Tackling Unique Problems by Using TWO SET Statements in ONE DATA Step	Ben Cochran	This paper illustrates solving many problems by creatively using TWO SET statments in ONE `DATA STEP`. Calculating percentages, Conditional Merging, Conditionally using Indexes, Table Lookups and Look ahead operations are investigated in this paper	set statement, point= option, conditional do loop, proc print, key=, proc step	9
63	The (Higher) Power of SAS	Andrea Frazier	Are there spiritual benefits to using SAS ? One synagogue in Chicago thinks so--and has its own all- volunteer informatics committee! The flexibility of SAS software is well-known for business applications, but SAS can also be used to improve membership data and evaluate programming, leading to a better congregant and community experience	deduplication, character functions	3
64	The Battle of the Titans (Part II): PROC TABULATE versus PROC REPORT	Kirk Paul Lafler, Ben Cochran, Ray Pass	Should I use `PROC REPORT` or `PROC TABULATE` to produce that report? Which one will give me the control and flexibility to produce the report exactly the way I want it to look? Which one is easier to use? Which one is more powerful? WHICH ONE IS BETTER? If you have these and other questions about the pros and cons of the REPORT and TABULATE procedures, this presentation is for you. We will discuss, using real-life report scenarios, the strengths (and even a few weaknesses) of the two most powerful reporting procedures in SAS (as we see it). We will provide you with the wisdom you need to make that sometimes difficult decision about which procedure to use to get the report you really want and need	proc step, proc report, proc tabulate, excel, proc means	14
65	The Building Blocks of SAS Datasets - S-M-U (Set, Merge, and Update)	Andrew T. Kuligowski	S-M-U. Some people will see these three letters and immediately think of the abbreviation for a private university and associated football team in Texas. Others might treat them as a three-letter word, and recall a whim sical cartoon character created by Al Capp many years ago. Ho wever, in the world of the SAS user, these three letters represent the building blocks for processing S AS datasets through the SAS `DATA STEP`. S, M, and U are first letters in the words SET, MERGE, and UPDATE - the 3 commands used to introduce SAS data into a `DATA STEP`. This presentation will discuss the syntax for the SET , MERGE, and UPDATE commands. It will compare and contrast these 3 commands. Finally, it will provide appropriate uses for each command, along with basic examples that will illustrate the main points of the presentation	dataset, set statement, merge statement, modify statement, update statement, by group processing, libname statement, out=, input statement, proc step, proc append, if-then-else, sysrc, stop, where statement, missing value	13
66	The Cox Hazard Model for Claims Data: Bayesian non-parametric approach	Samuel Berestizhevsky, Tanya Kolosova	General insurance protects individuals and organizations from financial losses due to damages or legal liabilities. It allows policyholders to exchange the risk of a large loss for the certainty of smaller periodic payments of premiums. Insurers allocate the premiums dollars into investment and cla ims payments. As it is for an insurer to manage its investment portfolio, it is equally important to manage its claims portfolio. Claim management is the analytics of insurance costs. It requires applying statistical techniques in the analysis and interpretation of the claims data. In the data-driven industry of general insurance, claim management provides useful insights for insurers to make better business decisions. The central piece of claim management is claims modeling. Two strategies are commonly used by insurers to analyze claims: the two-part approach that decomposes claims cost into frequency and severity components, and the pure premium approach that uses the Tweedie distribution	hazard function, proc phreg, survival function, hazard ratio, proportional hazards assumption, time-dependent covariate, workers compensation, survival analysis, loss event, insurance claims, markov chain, prior probability, sample, posterior probability, proc step	17
67	The Essentials of SAS Dates and Times	Derek P. Morgan	The first thing you need to know is that SAS software stores dates and times as numbers. However, this is not the only thing that you need to know, and this presentation will give you a solid base for working with dates and times in SAS. It will also introduce you to functions and features that will enable you to manipulate your dates and times with surprising flexibility. This paper will also show you some of the possible pitfalls with dates (and times and datetimes) in your SAS code, and how to avoid them. We'll show you how the SAS System handles dates and times through examples, including the ISO 8601 formats and informats, how to use dates and times in TITLE and/or FOOTNOTE statements, and close with a brief discussion of `EXCEL` conversions	sysdate9, date variable, macro variable, excel, missing value, proc format, flat file, proc step, error message, user-defined format, character variable, input statement, numeric variable, time, time., macro function	20
68	Tornado Inflicted Damages Pattern	Vasudev Sharma	On average, about a thousand tornadoes hit the United States every year. Three out of every four tornadoes in the world, occur in the United States. They damage life and property in their path and they often hit with very little, sometimes no warning. Tornadoes cause approximately 70 fatalities and 1,500 injuries in US every year. The interest of this study is to find a whether the fatalities and injuries caused by the tornadoes based on the weekday, magnitude are significantly different among the different levels. The idea behind this paper is to find patterns in the damages dealt by the tornadoes and find an insight whether the safety measures are applied correctly	anova, proc glm, sample, proc ttest, null hypothesis, post hoc test, proc genmod, proc step, heteroscedasticity, binary variable	11
69	Unconventional Statistical Models with the NLMIXED Procedure	Robin High		binomial distribution, poisson distribution, proc step, likelihood function, negative binomial distribution, negative binomial regression, regression analysis, proc nlmixed, geometric distribution, poisson regression, proc iml, proc glimmix, logsdf, estimate, cumulative frequency, data science, gamma distribution, maximum likelihood estimation, sdf, proc genmod, censoring	14
70	Untangle Customer's Incrementality using Uplift Modeling with a Case Study on Direct Marketing Campaign	Hairong Gu, Yi Cao, Chao Xu	Uplifting model is very appealing in direct marketing campaign, mainly because instead of predicting absolute propensity, it predicts the uplift in propensities of response or (sales) driven by promotion. When it is not guaranteed that the incremental campaign ROI will be maximized if customers with the highest propensities of response or sales are targeted, and marketing budget is limited, uplifting model is a better choice than a propensity model as it helps markers to select customers who would shop beca use of a promotion, instead of those who shop with or without the promotion. Despite of its obvious merit in practice, uplifting model is challenging to develop in practice. In this paper, we build two uplift models- uplift response model and uplift sales model for different business objectives with potential challenges and remedies are discussed. The methods are validated in a real-life marketing campaign from one of our clients	data cleaning, sample, proc hpforest, regression analysis, logistic regression, model training, predictor variable, sample size, logistic distribution	8
71	Using SAS to Compare Two Estimation Methods on the Same Outcome: Example from First Trimester Pregnancy Weights	Brandy R. Sinco, Edith Kieffer, Kathleen Welch, Diana Welmerink Bolton	Background. Accurate pre-pregnancy weight is important for weight gain recommendations during pregnancy. Weight gain is linear during first trimester. Objective. Use a linear mixed model (LMM), with random slope and intercept, to predict weight at the end of the first trimester (week 13), for 276 women, from a study in which many pre- pregnancy weights were self-reported and likely inaccurate. Compare the predicted weights at week 13 from the LMM to weights computed by adding a constant per week for 13 weeks. Methods. For a sub-sample in which the weights at week 13 were known, error variances between predicted and self-reported weights were compared with a `PROC MIXED` random effects model and then by using `PROC IML` to conduct a likelihood ratio test for a variance comparison. Graphically, `PROC SGPLOT` produced box plots and histograms to graphically display the variances between the two methods	proc step, imputation, proc mi, proc freq, cohen's kappa, roc curve, proc mixed, confidence interval, proc logistic, box plot, random intercept, logistic distribution, likelihood function, estimate, histogram, correlation	15
72	Waterfall Plots in Oncology Studies in the Case of Multi-Arms Design	Ilya Krivelevich, Kalgi Mody, Simon Lin	Clinical data are easier to understand when presented in a visual format. In Oncology, in addition to the commonly used survival curves, other types of graphics can be helpful in describing response in a study. These plots are becoming more and more popular due to their easy-to-understand representation of data. Waterfall plots can help to visualize tumor shrinkage or growth; in such plots, each patient in the study is presented by a vertical bar on the plot and each bar represents the maximum change in the measurement of tumors. In the studies with two arms, waterfall plots are often used to compare the outcome between arms. The excellent ground for understanding waterfall plots is proposed in the article of Theresa W. Gillespie, PhD, MA, RN: Understanding Waterfall Plots, Journal of the Advanced Practitioner in Oncology, 2012 Mar-Apr. This article claims that "A study using a randomization scheme other than 1:1 will not lend itself as well to a waterfall plot technique	waterfall chart, proc step, proc sgpanel, change from baseline, graph template language, randomization, output delivery system	15
73	What Are Occurrence Flags Good For Anyway?	Nancy Brucken	The ADaM Structure for Occurrence Data (OCCDS) includes a series of permissible variables known as occurrence flags. These are optional Y/null flags indicating the first occurrence of a particular type of record within a subject. This paper shows how occurrence flags can be used with `PROC SQL` to easily produce tables summarizing adverse events (AEs) by System Order Class (SOC) and dictionary preferred term	proc sql, proc step, adverse events	4
74	Working with Datetime Variable from Stata	Haiyin Liu, Wei Ai	Most SAS users have to transfer Stata data files into SAS frequently. However, you must be careful when converting Stata `DATETIME` to SAS `DATETIME`. In this paper, we examine how SAS or Stata stores `DATETIME` variables differently. We propose a correction function to accurately transfer Stata Da tetime into	datetime variable, proc import, datetime, elapsed time, dataset	3
75	Writing Code With Your Data: Basics of Data-Driven Programming Techniques	Joe Matise	In this paper aimed at SAS programmers who have limited experience with `DATA STEP` programming, we discuss the basics of Data-Driven Programming, first by defining Data-Driven Programming, and then by showing several easy to learn techniques to get a novice or intermediate programme r started using Data- Driven Programming in their own work. We discuss using `PROC SQL` SELECT INTO to push information into macro variables; `PROC CONTENTS` and the dictionary tables to query metadata; using an external file to drive logic; and generating and applying formats and labels automatically. Prior to reading this paper, programmers should be familiar with the basics of the `DATA STEP`; should be able to import data from external files; basic understanding of formats and variable labels; and should be aware of both what a macro variable is and what a macro is. Knowledge of macro programming is not a prerequisite for understanding this paper's concepts	proc step, macro variable, proc sql, sas programmer	9
76	You Did That With SAS? Combining Text with Graphics Output to Create Great Looking Reports.	Ben Cochran	This paper looks at methods of combining all kinds of output, such as text, graphs and tables, into a single pdf file. The process is accomplished totally within SAS. This paper is the product of having done this for an organization to improve its annual report process. What originally took a few months to produce, now takes only a few minutes	txt, proc step, proc report, conditional do loop	13

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