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BUGSWUSS 2017 Papers - Index
101 papers. Source: WUSS (Western Users of SAS Software) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | 2 Broken Axes in a Single Graph - That's Crazy Talk - How to Use PROC TEMPLATE, SGRENDER, and SGANNO To Make Your Graphs Look Awesome | Steven C. Black | Sometimes SAS has limitations that need to be worked around. These 'rare' circumstances also present golden opportunities to ask questions, learn new things, and experiment with options that you may have not worked with before. Such was the case with trying to create 2 broken axis in one graph. In this paper I illustrate how to utilize the Graph Template Language (GTL) and SGRENDER procedure to overcome some SAS graphing limitations. I also use annotations to help solve some labeling issues. I will go through each of the procedures used in a step by step manner to explain the options I utilized and the reasons behind them. The purpose of this paper is to explain how to create the figure and understand the syntax, but also to allow others to take these concepts and improve/build upon them. |
proc template, proc step, output delivery system, sample | source | 12 |
| 2 | A Critique of Implementing the Submission Data Tabulation Model (SDTM) for Drugs and Medical Devices | Carey G. Smoak | The Clinical Data Interchange Standards Consortium (CDISC) encompasses a variety of standards for medical research. Amongst the several standards developed by the CDISC organization are standards for data collection (Clinical Data Acquisition Standard Harmonization - CDASH), data submission (Study Data Tabulation Model - SDTM) and data analysis (Analysis Data Model - ADaM). Drug development was the original impetus for developing these standards. Therapeutic Area User Guides (TAUGs) have been a recent focus to provide advice, examples and explanations for collecting and submitting data for a specific disease. Non-subjects even have a way to collect data using the Associated Persons Implementation Guide (SDTMIG-AP). SDTM domains for medical device were published 2012. Interestingly, the use of Device domains in the TAUGs occurs in seventeen out of twenty-four of TAUGs providing examples of the use of the various Device domains. | clinical trial, clinical research, vital signs, adverse events, cdisc | source | 16 |
| 3 | A Data Mining Approach for Developing Intervention Programs to Lessen Body Dissatisfaction and Promote Healthy Weight Management | Pitipa Chongwatpol, Jongsawas Chongwatpol |
Due to the fact that body dissatisfaction is pervasive and is associated with many negative effects, it is important to recognize and begin to treat at an early developmental stage in order to prevent for the problem from becoming a more chronic condition. Many scholars and/or health professionals attempt to find effective interventions to alleviate this problem. This study applies a data mining approach to predict and explain the key determinants of body dissatisfaction from the data set of Thai adolescences. The main goal is to turn data into insights and identify factors that contribute to body dissatisfaction. The key findings indicate the key determinants of body dissatisfaction in more detailed and in different aspects from available literature, which can be used to support the development of intervention programs to lessen body dissatisfaction and promote healthy weight management. | proc hpsplit, regression analysis, stepwise selection | source | 12 |
| 4 | A Hands-on Introduction to SAS DATA Step Hash Programming Techniques | Kirk Paul Lafler | SAS users are always interested in learning techniques that will help them improve the performance of table lookup, search, and sort operations. SAS software supports a DATA STEP programming technique known as a hash object to associate a key with one or more values. This presentation introduces what a hash object is, how it works, the syntax required, and simple applications of it use. Essential programming techniques will be illustrated to sort data and search memory-resident data using a simple key to find a single value. |
hash table, proc transpose, lookup, proc step, sas log, composite key, dataset, proc sort, sas base | source | 12 |
| 5 | A Method for Simultaneously Programming Mock and Computed Tables | Michael Williams | A common task in planning a statistical analysis project is to create mock tables for a protocol or SAP. Mock tables can be made easily enough with a spreadsheet program or a word processing program. In this case, the creation of the mock table is completely detached from the subsequent creation of the (SAS programmed) computed table. In this paper, I will explain a method for creating macros that can create a mock table (based only on data set specifications) or a computed table. The driving engine of the macros is PROC SUMMARY with the COMPLETETYPES and PRELOADFMT options. |
proc means, proc step, nominal variable, interval variable | source | 8 |
| 6 | A Quick Look at Fuzzy Matching Programming Techniques Using SAS Software | Kirk Paul Lafler, Stephen B. Sloan |
Data comes in all forms, shapes, sizes and complexities. Stored in files and data sets, SAS users across industries know all too well that data can be, and often is, problematic and plagued with a variety of issues. When unique and reliable identifiers are available, users routinely are able to match records from two or more data sets using merge, join, and/or hash programming techniques without problem. But, what happens when a unique identifier, referred to as the key, is not reliable or does not exist. | proc step, proc sql, proc sort, character variable, proc freq, proc contents, order by, missing value, numeric variable, sas function, data merging, where statement, nominal variable, create table, data transformation, proc print, proc transpose, character functions, data cleaning | source | 25 |
| 7 | A Review of "Free" Massive Open Online Content (MOOC) for SAS Learners | Kirk Paul Lafler | Leading online providers are now offering SAS users with "free" access to content for learning how to use and program in SAS. This content is available to anyone in the form of massive open online content (or courses) (MOOC). Not only is all the content offered for "free", but it is designed with the distance learner in mind, empowering users to learn using a flexible and self- directed approach. As noted on Wikipedia.org, "A MOOC is an online course, content, or other resource aimed at unlimited participation and made available in an open access forum using the web." This presentation illustrates how anyone can access a wealth of learning technologies including comprehensive student notes, instructor lesson plans, hands-on exercises, PowerPoints, audio, webinars, and videos. | sas base, sas studio, sample | source | 16 |
| 8 | A SAS Format Catalog for ICD-9/ICD-10 Diagnoses and Procedures: Data Research Example and Custom Reporting Example | Robert Springborn | The Office of Statewide Health Planning and Development (OSHPD) provides patient level administrative data containing ICD-9 and ICD-10 diagnosis and procedure codes to users worldwide including: researchers; health care providers; health care insurance providers; and media. A SAS format catalog, which adds 161,360 ICD-9 and ICD-10 diagnosis and procedure code descriptions to administrative data, would greatly benefit all users and encourage new uses of administrative data by reducing the need for large ICD-9 and ICD-10 look-up manuals. This paper provides SAS program code for the SAS format catalog and both examples below. A research example shows the benefit to epidemiological studies of adding diagnosis and procedure code descriptions to a cohort constructed from administrative data. | format statement, dataset, excel, clinical data, sas library, sample, label statement, sas enterprise guide, data science, macro variable, proc step | source | 37 |
| 9 | A-NEST-ED Development: A One-Step PROC SQL Trick For Comparing Transactions To Their Own Summary Statistics Using Nested Functions | Ahmed Khan | Often, data must be compared to its own summary statistics in order to extract desired insights (as in financial, medical, and other transactions-based analyses). This usually requires creating summary statistics, adding conditions if/when necessary, joining source data back to summary data, and then comparing. My approach consolidates these steps into one. Building on Sunil Gupta's approach to remerge summary statistics with source data, my approach uses nested queries with HAVING and MAX/MIN functions to provide greater control over the type of summary statistics one desires, and then adds a CASE statement to create a new custom variable for easily comparison of detailed transactions vs summary statistics. | proc means, proc sql, group by, proc step | source | 7 |
| 10 | All Aboard! Next Stop is the Destination Excel | William E. Benjamin Jr. | Over the last few years both Microsoft EXCEL file formats and the SAS interfaces to those EXCEL formats have changed. SAS has worked hard to make the interface between the two systems easier to use. Starting with "Comma Separated Variable" files and moving to PROC IMPORT and PROC EXPORT, LIBNAME processing, SQL processing, SAS Enterprise Guide, JMP, and then on to the HTML and XML TAGSETS like MSOFFICE2K, and EXCELXP. Well, there is now a new entry into the processes available for SAS users to send data directly to EXCEL. This new entry into the ODS arena of data transfer to EXCEL is the ODS destination called EXCEL. This process is included within SAS ODS and produces native format EXCEL files for version 2007 of EXCEL and later. It was first shipped as an experimental version with the first maintenance release of SAS 9.4. This ODS destination has many features similar to the EXCELXP TAGSETS. |
excel, ods excel, proc step, output delivery system, xls, by group processing | source | 13 |
| 11 | An Easy Way to Split a SAS Data Set into Unique and Non-Unique Row Subsets | Thomas E. Billings | The Base SAS SORT PROCEDURE OPTIONS NOUNIQUEKEY and UNIQUEOUT= (new as-of SAS 9.3) provide an easy way to sort and split a SAS data set into 2 derivative subsets: one that contains only the rows that are unique per the BY variables (unique in the context of the entire input file), and another that contains all rows from the input file that have multiple instances of the BY variables. We show how these options are easier than equivalent legacy logic (a plain PROC SORT paired with a DATA STEP) and how this method produces results that are different from the split provided by the older NODUPKEY and DUPOUT= options. We also show how PROC SQL can be used to accomplish the same processing, and conclude that the new options for PROC SORT are an efficient way to split a data set based on unique/non-unique sets of BY variables. User level: beginner+. SAS products: Base SAS. Keywords: SORT, SQL, unduplication, unique rows. |
proc sort, proc step, proc sql, record count, select, sas base, sample | source | 8 |
| 12 | An Intro to PROC SQL: Simple & Complex Queries | Melissa Navarro | Structured Query Language (SQL) is the universal programming language for relational database management. SAS implementation of SQL is PROC SQL, which is an essential tool for managing and manipulating data. A query is an inquiry to retrieve data from one or more database tables by using a SELECT clause and other clauses such as FROM, WHERE, ORDER BY. This paper provides an introduction to both simple and complex PROC SQL queries. Additionally, I will present some tips that can benefit programmers when using PROC SQL. These skills are essential for anyone involved in analysis using SAS, and can serve as a foundation for those working with large data sets. |
proc sql, proc step, order by, right join, create table, group by | source | 9 |
| 13 | An Overview of SAS Visual Data Mining and Machine Learning on SAS Viya | Radhikha Myneni | Machine learning is in high demand. Whether you are a citizen data scientist who wants to work interactively or you are a hands-on data scientist who wants to code, you have access to the latest analytic techniques with SAS Visual Data Mining and Machine Learning on SAS Viya. This offering surfaces in-memory machine-learning techniques such as gradient boosting, factorization machines, neural networks, and much more through its interactive visual interface, SAS Studio tasks, procedures, and a Python client. Learn about this multi-faceted new product and see it in action. | sas studio, sas viya, machine learning, gradient boosting, proc step, logistic regression, proc hpsplit, data cleaning, regression analysis, score code, logistic distribution, support vector machine, libname statement, neural network, response variable, feature importance | source | 23 |
| 14 | Analysis of Complex Survey Data with SAS | Christine Wells | The differences between data collected via a complex sampling design and data collected via other methods need to be taken into account when analyzing complex survey data. The elements of the data unique to complex survey data are defined and discussed. Examples of procedures for descriptive statistics and graphs are given for continuous and categorical variables. The analysis of domains, sometimes called subpopulations, is discussed, followed by examples of ordinary least squares regression and logistic regression. | proc calis, sample, proc step, random sampling, regression analysis, proc mixed, by group processing, cluster sampling, format statement, proc surveymeans, nominal variable, proc means, proc surveyreg, sample weight, logistic regression, proc glimmix, where statement, proc freq, logistic distribution | source | 6 |
| 15 | Analyzing Multiple Membership Hierarchical Data Using PROC GLIMMIX | Maria Elsa Vazquez Arreola, Jeffrey Wilson |
Data collected based on a hierarchical structure are commonly seen in many fields such as education, social sciences, and medicine to name a few. For example, patients nested within doctors and doctors nested within hospitals or students nested within schools, schools nested within districts. Hierarchical data where lower level units are nested within a single upper level unit, for example, cases when patients only see one doctor are known as single membership. However, it is not always the case that lower level units are completely nested within only one upper level unit, as when a patient sees two or more doctors at a time; such nesting is known as multiple membership. Browne, Goldstein and Rasbash (2001) developed multiple membership multilevel models for continuous and binary data. We illustrate the use of PROC GLIMMIX in fitting multiple membership models to hierarchical binary data. |
proc mixed, proc glimmix, proc step, response variable, fixed effects, logistic regression, regression analysis, random intercept, logistic distribution | source | 7 |
| 16 | Approaches in Loan and Vintage Level ROI Forecasting - A Consumer Lending Industry Perspective | Sridhr Sankranti | This paper discusses various commonly used approaches to loan level and vintage level ROI forecasting (cumulative cash-on-cash return) in consumer lending industry. Typical consumer lending products considered in this paper include personal loans (fixed term installment loans) and equipment leasing with product term ranging from 6- to 18- month duration. The expected returns can be forecasted at loan (customer) level or at origination vintage level (aggregation of loans within a common origination period). Furthermore, the forecasts can be developed at the time of origination itself or after the first payment performance becomes available (static forecasts) or on a continuing basis (adaptive forecasts) as the loans age to maturity. | regression analysis, vintage analysis, proc step, proc reg, insurance risk score | source | 8 |
| 17 | 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, data cleaning, dataset, macro variable, sas output, sample, where statement, independent qc, proc report, macro parameter, macro definition | source | 34 |
| 18 | Automating Code Generation to Reduce Errors and Effort | Steve Cavill | This paper presents a series of solutions to automate code generation. This enables programmers to reduce effort and likelihood of errors by using some tools in SAS specifically for that purpose. In simple terms programmers can just write a macro and call the macro with variable parameters. This method still requires code editing. An alternative is to store the code variations in an external data set and have SAS read that data set and generate code automatically. This paper discusses techniques used to do this including CALL EXECUTE, CALL SYMPUTX and creating macro variables with PROC SQL INTO: clause. This technique has additional side benefits including reduced source code change control, simpler tracking of changes, and even the ability for the code changes to be managed by non-programmers This paper assumes reasonable knowledge of creating and using macro variables. |
macro variable, call execute, proc sql, call symputx, proc step, cpu time, data step, proc print, sas log, into, macro put, macro call, macro loop, macro quoting function | source | 14 |
| 19 | Avoiding Sinkholes: Common Mistakes During ADaM Data Set Implementation | Richann J. Watson, Karl Miller |
The ADaM Implementation Guide was created in order to help maintain a consistency for the development of analysis data sets in the pharmaceutical industry. However, since its inception we have seen issues with guideline non- conformance which can impede this development process and carry impacts that are felt down-stream in subsequent processes. When working with ADaM data sets, non-compliance and other related issues are likely the number one source for numerous hours of re-work; not only creating unnecessary additional work for the data sets themselves, but also for reports, compliance checks, the Analysis Data Reviewers Guide (ADRG), etc. all the way down to the ISS/ISE processes. Considering this breadth of impact, one can see how devastating these sinkholes can be. Like any sinkhole, there is a way out of it but it is a long, tedious process that will consume a lot of resources and it is always better to avoid the sinkhole entirely. | sdtm domain, imputation, raw data, change from baseline, crossover study, per protocol, interim analysis, treatment period, adverse events, cdisc | source | 15 |
| 20 | Building a Member Centric World from a Transactional Data Galaxy | Brian Mitchell | Health insurers have terabytes of transactional data. However, transactional data does not tell a member - level story. Humana Inc. is often faced with requirements for tagging (identifying) members with various clinical conditions such as diabetes, depression, hypertension, hyperlipidemia, and various member-level utilization metrics. For example, Consumer Health Tags are built to identify the condition (that is, diabetes, hypertension, and so on) and to estimate the intensity of the disease using medical an d pharmacy administrative claims data. This case study takes you on an analytics journey from the initial problem diagnosis and analytics solution using SAS. | proc sql, proc step, create table, order by | source | 3 |
| 21 | Building Intelligent Macros: 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 | source | 10 |
| 22 | Building Intelligent Macros: Using Metadata Functions with the SAS Macro Language | Arthur L. Carpenter | The SAS macro language gives us the power to create tools that to a large extent can think for themselves. How often have you used a macro that required your input and you thought to yourself "Why do I need to provide this information when SAS already knows it?" SAS may well already know what you are being asked to provide, but how do we direct our macro programs to self-discern the information that they need? Fortunately there are a number of functions and other tools within SAS that can intelligently provide our programs with the ability to find and utilize the information that they require. If you provide a variable name, SAS should know its type and length; given a data set name, the list of variables should be known; given a library or libref, the full list of data sets that it contains should be known. In each of these situations there are functions that can be utilized by the macro language to determine and return these types of information. | macro variable, macro facility, macro function, macro loop, do loop, dataset, sas log, proc step, proc contents, where statement, macro put, %if-%then-%else | source | 17 |
| 23 | Building Macros for Quick Survey Scoring | Vincent Chan, Lorena Ortiz |
Surveys are a critical research component when gathering information about a population of people. Researchers often implement surveys after a population has participated in a class or educational program with the aim to capture knowledge, attitudes, and experiences of participants. Survey developers create surveys using psychometric principles to ensure the reliability and validity of each question, also known as an 'item.' A developer can purposefully create sets of items, which when analyzed as a group, can measure a construct that describes the underlying behavior, attribute, or characteristic of a study participant. The programmer calculates the construct score by combining (e.g. summing or averaging) all component items of the construct. The programmer is tasked with creating such scores quickly. With an unlimited amount of time to spend, a programmer could operationalize the creation of these constructs by manually entering predefined formulas in the DATA STEP. |
macro variable, proc sql, macro loop | source | 6 |
| 24 | Cell Growth Simulation using SAS | Karen Walker | This paper will showcase the number crunching power of SAS 9.4. It's back to basics, with an updated twist, a s we will explore the power of the CAMERA interface coupled with SAS PROCS. Building files read into SAS 9.4, this discussion seeks to uncover broader technological trends by integrating visuals, with the metrics power of SAS 9.4. Our objective for this paper is to AUTOMA TE the process of visualization for cell growth in a way that inspires us. | proc step, web service, proc http, proc json, business intelligence | source | 8 |
| 25 | Creating a Data Dictionary Using Base SAS | Tasha Chapman | proc step, proc contents, data dictionary, sas base, variable list, output delivery system, dataset, excel, modify statement, proc datasets, proc sql, proc transpose, run | source | 13 | |
| 26 | Creating Completely Automated Reports in SAS Using the ODS RTF Destination: Protect Your Results from Human Error | Debra A. Goldman | Result reproducibility is an issue across disciplines, and unintentional human error is a major, but avoidable source of discrepant findings. Reporting an odds ratio of 1.9 instead of .19 can change the entire meaning of a study; having a $100,000 profit versus $1,000,000 can determine a company's solvency. Manually transferring results from SAS Output to a report increases the probability of errors, and automating report writing is a fantastic way to prevent major errors from occurring. By combining the abilities of ODS OUTPUT, ODS RTF destination, PROC ODSTEXT, and the SAS Macro language, one can move data directly from the dataset into a complete results report, including tables and text without having to manually type out findings. This will not only prevent errors, but also save time and enhance efficiency. This paper will demonstrate how to take results from SAS procedures, such as PROC FREQ and PROC LIFETEST, and move them into an RTF document using SAS 9.4 or later. |
proc step, proc report, proc freq, sample, macro variable, proc means, confidence interval, proc sql, character variable | source | 19 |
| 27 | Data Exploration for Sweet Spots by Using Heat Maps in SAS | Alec Zhixiao Lin | Sweet spots in data can be translated into valuable insights. Expert opinions and past experiences are two common sources for finding these spots, but they are inadequate in current era of big data when new information is constantly made available and new insights are constantly sought after. This paper introduces an efficient process in SAS that will examine all pairs of variables for visual exploration with minimal handling from users. The heat maps generated by the process will help users to identify sweet spots for a discovery of new knowledge or for downstream analyses. | data validation rules, heatmap, proc sort, proc step, regression analysis | source | 7 |
| 28 | Decision Trees: a Gentle Introduction | Richard Hector | Every car is a vehicle, but not every vehicle is a car. Similarly, classification and regression trees (CART) and decision trees look similar. Both begin with a single node followed by an increasing number of branches. However, they serve different purposes. The purpose of a family automobile is different from that of giant mining truck. This paper is a gentle introduction to decision trees using PROC DTREE. When you need to explore the relationship to factors and an outcome, CART is a useful non-parametric tool. The branching algorithm facilitates moving through the variables in the data to determine their effect on the outcome. In contrast, in decision trees the variables are chosen because the decision maker knows or can infer their effects on the outcome. Also, the decision maker knows or can estimate their distribution among relevant groups. Finally, the result (reward or pay-off) of each pathway is known or can be estimated. |
proc hpsplit, decision analysis, proc step, in= | source | 11 |
| 29 | Doing More with the SAS Display Manager: From Editor to ViewTable - Options and Tools You Should Know | Arthur L. Carpenter | If you have used the interactive interface for SAS you have most likely used the Display Manager. As it ships, the Display Manager is very powerful and yet fairly easy to use with a minimal learning curve for the new user. Because it is functional 'right out of the box', most users do very little to customize the interface. This is a shame, because the Display Manager contains a great many hidden opportunities to make it even more powerful, even easier to use, and customized for your way of using the interface. The Display Manager builds a variety of windows, screens, and dialogue boxes to facilitate communication between SAS, the Operating System, and the user. For each of the five primary windows and extending to the dozens of secondary windows there are options that control the content, display, and level of interaction. Op tions have defaults and a majority of these can be easily changed to suite your needs. | proc step, dataset, macro call, proc print, macro facility, macro variable, macro put, macro quoting function | source | 17 |
| 30 | Effectively Utilizing Loops and Arrays in the DATA Step | Arthur X. Li | The implicit loop refers to the DATA STEP repetitively reading data and creating observations, one at a time. The explicit loop, which utilizes the iterative DO, DO WHILE, or DO UNTIL statements, is used to repetitively execute certain SAS statements within each iteration of the DATA STEP execution. Utilizing explicit loops is often used to simulate data and to perform a certain computation repetitively. However, when an explicit loop is used along with array processing, the applications are extended widely, which includes transposing data, performing computations across variables, etc. Being able to write a successful program that uses loops and arrays, one needs to know the contents in the program data vector (PDV) during the DATA STEP execution, which is the fundamental concept of DATA STEP programming |
do loop, output, execution phase, array, in=, conditional do loop, out=, ranuni, dataset, missing value, compilation phase, array bounds, implicit loop, temporary array, set statement, standard deviation, automatic variable, class variable, normal distribution | source | 17 |
| 31 | Examining Distributional Shifts by Using Population Stability Index (PSI) for Model Validation and Diagnosis | Alec Zhixiao Lin | Population stability index (PSI) is a metric to measure how much a variable has shifted in distribution between two samples or over time. It is widely used for monitoring changes in the characteristics of a population and for diagnosing possible problems in model performance. This paper introduces an efficient process in SAS that will automatically compare two samples by examining all model-related categorical and numeric variables with minimal manual handling needed from users. The output provides useful statistics and visuals of how much each variable has shifted in distribution. | sample, data cleaning | source | 7 |
| 32 | Examining the Performance of a Control Chart for the Shifted Exponential Distribution using Penalized Maximum Likelihood Estimators: A Simulation Study using SAS | Austin R. Brown, Bryce Whitehead |
For many years, control charts have been utilized to monitor processes, improve quality, and increase profitability. However, the body of literature tilts overwhelmingly toward charts monitoring normally distributed processes. In practice, the underlying distribution of a pro- cess may not follow a normal distribution, and many of those techniques may not be most effective. Mukherjee, McCracken, and Chakraborti (2015) suggested three control charts for simultaneous monitoring of the location and scale parameters for processes following the shifted exponential distribution. This study examines their proposed, Shifted Exponen- tial Maximum Likelihood Estimator-Max Chart, (SEMLE-max) and suggests utilization of penalized maximum likelihood estimators (MLE) instead of traditional MLEs because of un- biasedness and minimum variance among unbiased estimators. | sample, likelihood function, exponential distribution, normal distribution, maximum likelihood estimation, proc iml, proc step | source | 8 |
| 33 | Exploratory Analysis of the Factors Related to Gun Mortality | Gabrielle Ilenstine, Stephanie Mendoza, Evelyn Fox |
Every year, tens of thousands of Americans are killed in a gun related accident, suicide, or homicide. This tragically high number of deaths, along with the idea that many of these incidents could have been prevented, has put the topic of gun and firearm reform in the spotlight of political and social debate. However, defunding of publicly funded gun-related research has led to inadequate knowledge for creating an effective prevention plan. It is clear that local, state, and federal policy leaders, gun owners, and most importantly: the general public, need better information in order to make educated decisions. | proc hpforest, feature importance | source | 17 |
| 34 | 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 | source | 8 |
| 35 | Five Things You Should Know about Quantile Regression | Robert N. Rodriguez | The increasing complexity of data in research and business analytics requires versatile, robust, and scalable methods of building explanatory and predictive statistical models. Quantile regression meets these requirements by fitting conditional quantiles of the response with a general linear model that assumes no parametric form for the conditional distribution of the response; it gives you information that you would not obtain directly from standard regression methods. Quantile regression yields valuable insights in applications such as risk management, where answers to important questions lie in modeling the tails of the conditional distribution. Furthermore, quantile regression is capable of modeling the entire conditional distribution; this is essential for applications such as ranking the performance of students on standardized exams. | regression analysis, proc quantreg, predictor variable, data cleaning | source | 19 |
| 36 | Five Ways to Flip-Flop Your Data | Joshua M. Horstman | Data is often stored in highly normalized ("tall and skinny") structures that are not convenient for analysis. The SAS programmer frequently needs to transform the data to arrange relevant variables together in a single row. Sometimes this is a simple matter of using the TRANSPOSE procedure to flip the values of a single variable into separate variables. However, when there are multiple variables to be transposed to a single row, it might require multiple transpositions to obtain the desired result. This paper describes five different ways to achieve this flip-flop, explains how each method works, and compares the usefulness of each method in various situations. Emphasis is given to achieving a data-driven solution that minimizes hard-coding based on prior knowledge of the possible values each variable can have and that improves maintainability and reusability of the code. |
proc transpose, proc step, response variable, by group processing, merge statement, proc sql, sas programmer, keep statement, sample | source | 10 |
| 37 | 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 | source | 6 |
| 38 | 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. Troy Hughesi describes a process using SAS macro language, the DLCREATEDIR option, and control tables to facilitate and document the logical folder creation process. This paper describes a technique wrapping another layer of macro processing which isolates and expands the recursive logical folder assignment process to create a complex, hierarchical folder structure used by the author for a project requiring monthly data intake, processing, quality control and delivery of thousands of files. Analysis of the prior month's folder structure to inform development of control tables and build executable code is discussed. | dlcreatedir option | source | 9 |
| 39 | HASH Beyond Lookups - Your Code Will Never Be the Same! | Elizabeth Axelrod | If you've ever used the hash object for table lookups, you're probably already a fan. Now it's time to branch out and see what else hash can do for you. This paper shows how to use hash to build tables and aggregate data. Why would you ever want to do this? How about achieving a complex process that would have taken multiple sorts and many passes through your data - all in a single DATA STEP that flows intuitively and isn't hard to write! I learned this technique from a presentation by hash masters Paul Dorfman and Don Henderson, and now my code - and how I think about solving problems - will never be the same. |
hash table, hash iterator, lookup | source | 7 |
| 40 | Hi-Velocity SAS Coding: Applying Engineering Principles to SAS Programming Tasks | Joe Perry | Sometimes you manage, or work, on a project that seems, well, impossible to do with the resources available; not enough time, not enough SAS programmers, too complex… not enough of anything or too much of everything to do the SAS programming job within the time and resource constraints! When confronted with the condition of too little time, too few resources and an absolute need to complete a task within those resource constraints, Industrial Engineers in the manufacturing and process industries get to work redesigning the process to work within those new realities they must face. Though no large-scale movement of SAS jobs to off-shore programming houses has yet occurred, the handwriting is already on the wall; compete or die will have to become the SAS programming mantra. The reality is, SAS programming, and programming tasks, could benefit from the industrial engineering techniques used in the manufacturing, process and programming worlds. | sas programming, sas programmer | source | 6 |
| 41 | How SAS Thinks: SAS Basics I | Susan J. Slaughter | dataset, sas studio, proc step, libname statement, sas log, date variable, sas enterprise guide | 36 | ||
| 42 | I've Got to Hand It to You; Portable Programming Techniques | Arthur L. Carpenter, Mary F. O. Rosenbloom |
As technology expands, we have the need to create programs that can be handed off - to clients, to regulatory agencies, to parent companies, or to other projects, and handed off with little or no modification by the recipient. Minimizing modification by the recipient often requires the program itself to self-modify. To some extent the program must be aware of its own operating environment and what it needs to do to adapt to it. There are a great many tools available to the SAS programmer, which will allow the program to self-adjust to its own surroundings. These include location-detection routines, batch files based on folder contents, the ability to detect the version and location of SAS, programs that discern and adjust to the current operating system and the corre sponding folder structure, the use of automatic and user defined environmental variables, and macro functions that use and modify system information. Need to create a portable program? We can hand you the tools. | macro variable, options statement, sas log, automatic macro variable, macro put, macro function, macro definition, autocall macro, macro flow control, libname statement, put statement, macro call, sas enterprise guide, macro facility | source | 21 |
| 43 | Identifying Unforeseen Strains on Local Health Care Ecosystems | Evan Wallis | Do patients in primary care shortage areas leave their local community for healthcare services? If so, do these patients impact the areas where they seek treatment? This paper describes an analysis that determined where patients throughout California go for healthcare services. OSHPD conducted an initial analysis that found elevated chronic disease rates (above statewide median) for many primary care shortage areas within California. In further analysis, OSHPD found an abundance of patients in primary care shortage areas traveling to neighboring communities for healthcare services. Consequently, this may be causing a strain on healthcare providers in the neighboring communities, affecting residents' access to care and overall health outcomes. Researchers at OSHPD utilized SAS Enterprise Guide (EG) to merge several datasets and conduct an analysis to determine where patients go for hospital care. | sas enterprise guide, data visualization | source | 7 |
| 44 | Importing CSV Data to All Character Variables | Arthur L. Carpenter | Have you ever needed to import data from a CSV file and found that some of the variables have been incorrectly assigned to be numeric? When this happens to us we may lose information and our data may be incomplete. When using PROC IMPORT on an EXCEL file we can avoid this problem by specifying the MIXED=YES option to force all the variables to be character. This option is not available when using IMPORT to read a CSV file. Increasing GUESSINGROWS can help, but what if it is not sufficient? It is possible to force IMPORT to only create character variables. Although there is no option to do this, you can create a process that only creates character variables, and the process is easily automated so that no intervention is required on the user's part. |
proc import, proc step, dataset, character variable | source | 5 |
| 45 | Integrating Health Data Sources to Identify, Stratify, and Predict High Utilizers of Public Systems | Benjamin Knisley, Joshua Morgan, Linn Carothers, Keith Haigh |
The California Medi-Cal 2020 waiver includes a five-year pilot program known as Whole Person Care (WPC), which focuses on health interventions that coordinate physical health, behavioral health, and social service needs of beneficiaries who are high users of multiple county entities. San Bernardino County developed an analytic approach combining and matching health and social services data from multiple County departments utilizing disparate systems and identifiers. This required collaboration of data experts in multiple County departments to produce the most accurate matching approach. In many high utilizer projects, potential service recipients are identified through costs or basic utilization. However, both of these have limitations and may miss individuals who are not appropriately engaged in their care or their care is not appropriately coordinated. | logistic regression, logistic distribution, regression analysis, data cleaning, interval variable, likelihood function, predictor variable, main effect, model validation, proc freq, correlation | source | 15 |
| 46 | Introduction to DATA Step Programming: SAS Basics II | Susan J. Slaughter | raw data, set statement, input statement, dataset, merge statement, sas log, proc step | 39 | ||
| 47 | Introduction to SAS Procedures: SAS Basics III | Susan J. Slaughter | proc step, proc print, proc contents, proc freq, proc sort, proc means, output delivery system | 40 | ||
| 48 | Is My Model the Best? Methods for Exploring Model Fit | Deanna Naomi Schreiber-Gregory | In the submission/presentation phase of any research or analytics project, it is reasonable to expect the reception of many types of questions aimed at clarifying the reliability and accuracy of the project's results. One of the most common questions to expect would be: "So the model provides a feasible answer to the question, but does it provide the best answer?" One way to answer this question with utmost confidence is to provide a variety of model fit analyses designed to support the conclusion of your final model. This paper provides a variety of techniques aimed at model fit exploration including default procedure settings as well as additive options. This paper will review the theory behind each of these fit procedures and the pros and cons of their use. Optional R-square calculations will also be explored. This paper is intended for any level of SAS user. This paper is also written to an audience with a | proc step, akaike information criterion, bayesian information criterion, proc logistic, logistic distribution, response variable, proc genmod, proc reg, regression analysis, proc phreg | source | 7 |
| 49 | Job Upward Mobility: Getting Better When You're Already Good | Molly Hall | You're a SAS Programmer, or a Statistical Analyst, or a Data Scientist, or a Manager of SAS Programmers. You make a healthy six figures, or close to it. Perhaps you drive a Lexus, or other luxury car. You're reasonably happy in your career. Still, sometimes you can't help feeling a little stuck. You're curious about exploring opportunities, but you want to do it inconspicuously, and you don't want to jeopardize your current position. We will talk about the SAS job market. We will explore networking channels that work. Can using Social Media boost your career? Is it appropriate? Let's discuss. Some people say that resumes are dead - is this true? What about the cover letter - is it necessary? We will also examine interview tips and techniques. Even if you are happy at your current company, or in your current job, this presentation is for everyone, and it is appropriate for people at all levels. It's time to stop procrastinating and, perhaps, level up | data science, predictive analytics | source | 11 |
| 50 | Keeping Passwords, AES Encryption Keys, and Other Sensitive Parameters Out of Source Code and Logs | Thomas E. Billings | The use of metadata-bound, encrypted libraries (with meta-LIBNAMEs) is one way to keep SAS data set passwords and encryption keys out of source code and logs. However, that approach is constrained to the client-server metadata environment and data set/LIBNAME access, and at m any sites requires SAS Admin support. This paper builds on previous work (Billings 2017A) and presents a simple, easy, secure, non-metadata method for keeping passwords, encryption keys and other sensitive/confidential single - string/token parameters out of the code and log. Compiled, encrypted function-style macros - written in a specific way - are used to supply the sensitive information. Compiled, encrypted macros are not as secure as one might expect; methods are described here to check for and mask sensitive parameters in the executable form of compiled macros. This approach was originally developed to supply (and protect) AES encryption keys and SAS-proprietary passwords for SAS data sets. |
stored macro, proc step, macro variable, options statement, macro definition, macro quoting function, error message, sas enterprise guide, proc sql, excel, macro catalog | source | 13 |
| 51 | Learnings and Pitfalls from My EG Migration | Kent Lewandowski | SAS Enterprise Guide (EG) introduced a whole new way to manage and execute SAS projects. It has many unique and useful features: an interactive log, "process flow" interface, branch run control, and starting with version 7.1, smart highlighting and the ability to track source code changes. EG version 7.1 was released at the end of 2014, yet in my enterprise, it is only now being deployed. I've had the chance to review both versions 6.1 and 7.1. and compare them side by side. Drawing on my experience, as well as past presentations by other experts, I will highlight some of the useful changes in EG Version 7.1, as well as some of what I perceive to be additional opportunities for improvement. The presentation is intended for users with beginner to intermediate SAS skills who are either somewhat familiar with SAS Enterprise Guide, or else would like to learn more about it. | sas enterprise guide, sas base | source | 18 |
| 52 | Maintaining ORDER in Your (PROC) Reporting | Bill Coar | In creating summary tables or listings of raw data, PROC REPORT is a clear and obvious choice within the pharmaceutical industry. Summary statistics are often placed in a reporting dataset to be displayed using PROC REPORT. In doing so, it is extremely common for the first few columns to be ORDER variables. While this helps with sorting, the primary reason for using an ORDER variable is so that the value only appears on the first record when displaying multiple rows with the same value of the ORDER variable. Furthermore, the value repeats on subsequent pages when the records (with the same ORDER value) span multiple pages. However, when using an ODS destination this is not always the case. This quick tip will assume that users have a (very) basic understanding of PROC REPORT and the use of ODS destinations of RTF and PDF. The use of ODS is required in this application using SAS 9.4 in a Windows environment. |
proc report, proc step, binary variable, trial elements | source | 5 |
| 53 | Making a Difference With SAS | Humberto Cisneros, Carrin Huff |
This paper discusses the process of how SAS is used to analyze Arizona limited jurisdiction court data to make a difference in people's lives. This paper will not discuss any new SAS programing methods, SAS functions or pro cedures. This paper will presents four topics: data used in the analysis, how data was manipulated, analysis results, and impact to the community. Justice of the Peace and Municipal Courts in Arizona are courts of limited jurisdiction. The case types these courts adjudicate vary widely, from felony to civil traffic violations, such as speeding. They also handle civil cases such as evictions, small claims, civil claims under $10,000, and orders of protection. SAS programs were used to analyze how defendants were adjudicated. By analyzing the court data we were able to determine how many individuals either pled guilty or were found responsible. | data science, proc format | source | 15 |
| 54 | Many Means to a Mean | Shannon Pileggi | This paper is motivated by reflection on an exam question in an introductory SASR⃝ programming course: "Suppose we have a SAS data set with three exam grades from ten students. How could you get the average score for each exam? How could you get each student's average exam grade?" Students often confused the application of PROC MEANS and the MEAN function in a DATA STEP for achieving their objectives. In this paper, we provide an overview of various methods to achieve either column-wise means or row-wise means, which include DATA STEP methods (with functions or cumulative sums) and procedures ( MEANS, TABULATE, REPORT, and SQL). We also discuss various characteristics of these methods, including the difficulty level of the syntax, how missing data is treated, and presentation of the output. This paper will be accessible to novice SAS users as well as provide more experienced users alternative methods for calculating a mean. |
proc step, proc tabulate, numeric functions, proc sql, missing value, proc report, proc means, dataset | source | 9 |
| 55 | Maximizing Cross-Sell Opportunities with Predictive Analytics for Financial Institutions | Nathaniel Derby | In the increasingly competitive environment for banks and credit unions, every potential advantage should be pursued. One of these advantages is to market additional products to our existing customers rather than to new customers, since our existing customers already know (and hopefully trust) us, and we have so much data on them. But how can this best be done? How can we market the right products to the right customers at the right time? Predictive analytics can do this by forecasting which customers have the highest chance of purchasing a given financial product. This paper provides a step-by-step overview of a relatively simple but comprehensive approach to maximize cross-sell opportunities among our customers. We first prepare the data for a statistical analysis. With some basic predictive analytics techniques, we can then identify those customers who have the highest chance of buying a financial product. | data cleaning, gain chart | source | 16 |
| 56 | 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 | source | 14 |
| 57 | Methods to Identify and Validate SAS-Proprietary and AES Encrypted SAS Data Sets | Thomas E. Billings | In a specialized context, this paper demonstrates how to read SAS data sets and non-SAS files inside a DATA STEP using functions, and how to interrogate the SYSMSG() function to check for errors. Knowing how to do this is useful in a much wider context in the SAS ecosystem. The specific application addressed here: given a file that may or may not be a SAS data set, how can we check if it is an encrypted SAS data set? This is important for SAS server sites that rely on SAS-provided encryption to satisfy production requirements that "data at rest" must be encrypted. The task is trickier than expected as we show that encryption limits your ability to read (or even see) encrypted files in the SAS system. For files that follow SAS naming conventions, we first describe simple, predominantly manual methods to test for encryption - methods that produce error messages in the log when a file is encrypted. |
dataset, error message, proc step, proc print, dictionary tables, macro variable, options statement, type ii error, proc datasets | source | 15 |
| 58 | Minimize PROC REPORT Time and Maximize Your Beach Time | Nadira Babaeva | proc report, proc step, macro variable, do loop, proc contents, ods rtf, macro definition | source | 8 | |
| 59 | Modeling Longitudinal and Multilevel Data in SAS | Niloofar Ramezani | Correlated data, including longitudinal and multilevel data, are extensively used across disciplines when modeling data with any type of correlation that may exist among observations due to repeated measurements or clustering. This is what makes it crucial to ensure applying the appropriate tools in order to efficiently model such data in different disciplines. When modeling longitudinal data, especially when dealing with discrete responses over time, more challenges arise, hence more complicated models are required. Such models that enable researchers and applied practitioners to properly account for the autocorrelation among repeated observations can b e categorized into three main techniques: conditional, transitional, and marginal models (Firzmaurice et al., 2009). Examples of such models include Generalized Linear Mixed Model (GLMM), Generalized Estimating Equations (GEE), Generalized Method of Moments (GMM), and Alternating Logistic Regression (ALR). | correlation, proc step, response variable, proc mixed, fixed effects, longitudinal data, proc genmod, random slope, regression analysis, proc glimmix, proc model, conditional logistic regression, random intercept, repeated measures | source | 8 |
| 60 | Modernizing and Integrating SAS and non-SAS Tools and Systems for Behavioral Health Data Collection, Processing, and Reporting" | Manuel Gomez, Keith Haigh, Joshua Morgan |
Access to effective metrics for behavioral health program staff is vital for on-going monitoring of program efficacy, but making that data usable and accessible can be a challenge, especially when using legacy data systems. Modernizing data reporting and integrating data tools from dissimilar data collection and analysis systems can be a complex, challenging process, especially while trying to maintain support for on-going reporting used by non-technical program staff. Converting to new SAS analysis systems can be intimidating for non-SAS users, creating a barrier to the efficient production information particularly when older less efficient processes are already in place. This paper describes the methodology used for integrating SAS, MS EXCEL, and a cloud-based dashboarding tool to best leverage different tools accessible to end users. |
excel, sas enterprise guide project, dataset, sas enterprise guide | source | 18 |
| 61 | Modernizing Legacy SAS Applications and Program Code | Kirk Paul Lafler, Clark Roberts |
Whether you are a novice or experienced SAS programmer with responsibility for the support of your organization's legacy applications, programs and code, assistance is available to help modernize and streamline code for the 21st century and beyond. This paper and presentation explores the available constructs, statements, functions, algorithms, operators, methods, expressions, programming techniques and approaches users have to update and modernize legacy applications, programs and code first introduced as far back as the 1970's. Attendees learn how to use the SCAPROC procedure - the SAS code analyzer - to analyze metadata about the contents of SAS code, and to streamline, scale and modernize code constructs, statements, functions, algorithms, and legacy applications and program code. Table of Contents Abstract ................................................................................................................................................................. |
proc step, if-then-else, proc sql | source | 51 |
| 62 | Multicollinearity: What Is It and What Can We Do About It? | Deanna Naomi Schreiber-Gregory | Multicollinearity can be briefly described as the phenomenon in which two or more identified predictor variables in a multiple regression model are highly correlated. The presence of this phenomenon can have a negative impact on the analysis as a whole and can severely limit the conclusions of the research study. This paper reviews and provides examples of the different ways in which multicollinearity can affect a research project, and tells how to detect multicollinearity and how to reduce it once it is found. In order to demonstrate the effects of multicollinearity and how to combat it, this paper explores the proposed techniques by using the Youth Risk Behavior Surveillance System data set. This paper is intended for any level of SAS user. This paper is also written to an audience with a background in behavioral science or statistics. | regression analysis, predictor variable, correlation, ridge regression, estimate, response variable, condition index, sample, proc princomp, proc reg, multiple regression, proc corr | source | 12 |
| 63 | Next Best Offer Analytics using SAS | Robin E. Way, William Eisenhauer |
There are many campaign designs that will produce strong results, which are produced by the ideal mix of offer strategy, timing, incentives, delivery, controls, fulfillment, brand, and of course, previously unmet customer needs. The pinnacle of cross-sell campaign performance is reached by having the ideal allocation of next best offers ("NBOs") to customers via a mathematical optimization-enabled campaign discipline, practices and tools. Key findings are (a) fiscal contribution of cross-sell marketing campaigns can be significantly enhanced by adopting mathematical optimization approaches that augment traditional campaign strategies, (b) adoption of mathematical optimization for marketing campaigns requires skills, patience and diligence, and experience of other marketing organizations in using mathematical optimization to build next-best-offer cross-sell strategies will help your organization reach similar objectives faster and with more organizational buy-in. | odds ratio, data validation rules, financial services, proof of concept, likelihood function, credit card, cross-sell, scoring | source | 12 |
| 64 | NFL Data Analytics for a New Era | Mitchell Collins | logistic regression, logistic distribution, regression analysis | source | 14 | |
| 65 | Nonlinear Modeling with Big Data in SAS and JMP | Chong Ho, Hyun Seo Lee, Siyan Gan, Emily Brown |
Very often conventional statistical procedures, such as Pearson correlation and regression modeling, impose an unrealistic linear structure on the data. However, in the real world many relationships are curvilinear. When the sample size is very large, it is difficult to detect non-linear patterns in the over-plotted data cloud. In this presentation, the data sets of 2015 Programme for International Student Assessment (PISA) and 2016 Programme for International Assessment of Adult Competencies (PIAAC) were utilized to demonstrate how self-efficacy in science and self-report readiness to learn are correlated with test performance in a nonlinear fashion. The SAS and JMP procedures for detecting these nonlinear patterns include Lambda smoothing spline via interactive data visualization, artificial neural networks, and transformation regression. Merits and weaknesses of each technique are discussed. | regression analysis, neural network, proc nlin, machine learning, sample | source | 12 |
| 66 | One-to-One, One-to-Many, and Many-to-Many Joins Using PROC SQL | Charu Shankar, Kirk Paul Lafler |
A powerful and essential PROC SQL programming technique that all SAS users should understand, and be comfortable performing, is the process of joining (or combining) two or more tables of data. This paper and presentation describes and illustrates the join process, including what a join is, exploration of one-to-one, one-to-many, and many-to-many data relationships, identifying a primary key (or unique identifier), special preparation requirements for each table being specified in a join, and popular join techniques available to SAS users. To demonstrate the power of the join process, examples of conventional (symmetrical matching) and unconventional (asymmetrical left, right and full matching) using PROC SQL join programming techniques are illustrated. |
proc sql, data merging, where statement, inner join, proc step, full join, right join | source | 19 |
| 67 | Open Source Platform Unveiled | Karen Walker | This paper will show you how to install OPEN SOURCE platform software and deploy it on your computer. The paper reveals how to use special analytic software "SAS Viya", running under UNBUNTU. | sas viya, quality control, sample | source | 9 |
| 68 | Parallel Processing Your Way to Faster Software and a Big Fat Bonus: Demonstrations in Base SAS | Troy Martin Hughes | SAS software and especially e xtract-transform-load (ETL) systems commonly include components that must be serialized due to real process dependencies. For example, a transform module often cannot begin until the data extraction completes, and a corresponding load module cannot begin until the data transformation completes. While process dependencies such as these cannot be avoided in many cases and necessitate serialized software design, in other cases, programs or data can be distributed across two or more SAS sessions to be processed in parallel , facilitating significantly faster software . This text introduces the concept of false dependencies, in which software is serialized by design rather than necessity , thus needlessly increasing execution time and deprecating performance. | macro variable, parallel processing, proc freq, proc corr | source | 8 |
| 69 | Pattern of Adverse Events: The Use of Box Plot | Jui-Fu Huang | Boxplot is used as a simple way to display the distribution of numerical data through their quartiles. In order to recognize if certain adverse events develop earlier than the others after being treated with study product, this paper introduces boxplot as a quick and easy way to present such pattern. | adverse events, box plot | source | 5 |
| 70 | Pinching Off Your SAS Log: Shifting the Perception of "SAS Logs" from a Post Hoc Validation Used Haplessly and Often Too Late...to a Dynamic Quality Control That Drives Exception Handling and Program Flow | Troy Martin Hughes | Too often in SAS literature, the role of the program log is narrowly conceptualized as a static, post hoc quality control review that validates program success through the detection of program failure or the lack thereof. Especially when software development occurs outsid e of a formalized software development life cycle (SDL C), as is often the case with non-production software and with in end-user development environments, SAS practitioners must painfully parse logs in search of notes, warnings, runtime errors, and other of ten elusive indications of functional failure. A substantial body of SAS literature advances antediluvian manual log review through the automation of log parsing and analysis and subsequent communication of program success (or failure) to stakeholders. To a lesser extent, SAS literature depicts how log parsing can be utilized to extract, analyze, and ultimately improve software performance metrics. | key performance indicator, sas log, proc sort, cpu time, stress testing, macro variable, if-then-else, repeated measures, repeated measures anova, txt, macro definition, sample, proc sql, character variable, data cleaning, exception handling, sysrc, numeric variable, parallel processing, proc printto, quality control, options statement, proc step, macro loop | source | 26 |
| 71 | 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, label statement, excel | source | 8 |
| 72 | PROC TABULATE Made Easy | Yunin Ludena, Elizabeth Guerrero Angel |
SAS provides users with multiple procedures for computing descriptive statistics and displaying summary tables, but nothing sounds more appealing than generating tables using PROC TABULATE. This is a sentiment not shared by many because PROC TABULATE has a less intuitive syntax than other SAS procedures. But, don't worry, because in this paper we wil l explore what PROC TABULATE has to offer in an easy to understand format. We will start by showing the basic PROC TABULATE syntax and how to manipulate the statements to get the desired, concise table, including single and multidimensional tables. Then, w e will demonstrate, using a predefined SAS data set, how to add descriptive statistics, handle missing data and add ODS styles to your tables. We will also include some useful tips and tricks to make your PROC TABULATE experience enjoyable. |
proc tabulate, proc step, class variable, proc format, proc means, missing value, by group processing | source | 15 |
| 73 | Programming Triage: Rooting Out Dataset Discrepancies in the Validation Process | Julia Sturgeon | We have all encountered it before: 'sick' datasets. Most of the time, these are analysis or standardized datasets that are used in downstream production, such as tables, listings and figures. Sometimes the 'illness' in a dataset goes unnoticed until an independent programmer attempts to compare it to their interpretation of what the dataset should and should not have. In the CRO environment, the ability to quickly and efficiently diagnose quality control issues in an analysis or standardized dataset is a critical skill for survival. Often times the discrepancies can cause a cascade of problems in quality of work and later interpretation of results. As an independent validator of an analysis or standardized dataset, it is helpful to be armed with a 'doctor's bag' of tools to use in the validation process. This paper outlines some of the easy ways PROC COMPARE and PROC FREQ can be used to root out underlying disease in a dataset. |
proc compare, proc step, data cleaning, proc freq | source | 7 |
| 74 | Propensity Score Methods for Causal Inference with the PSMATCH Procedure | Maura E. Stokes | In a randomized study, subjects are randomly assigned to either a treated group or a control group. Random assignment ensures that the distribution of the covariates is the same in both groups and that the treatment effect can be estimated by directly comparing the outcomes for the subjects in the two groups. In contrast, subjects in an observational study are not randomly assigned. In order to establish causal interpretations of the treatment effects in observational studies, special statistical approaches that adjust for the covariate confounding are required to obtain unbiased estimation of causal treatment effects. One strategy for correctly estimating the treatment effect is based on the propensity score, which is the conditional probability of the treatment assignment given the observed covariates. | propensity score, propensity score matching, treatment effect, response variable | source | 17 |
| 75 | Quality Control of Clinical Data Listings with PROC COMPARE | Robert Bikwemu, Nicole Wallstedt |
Checking clinical data listings with PROC COMPARE is a quick way to validate the order, completeness, and content of the listings. This is valuable and efficient when working with hundreds or thousands of pages per listing. Here we introduce the setup steps which need to be taken by both developer and quality control (QC) programmer, and suggested steps a QC programmer can take when validating large listings. |
proc step, proc compare, proc freq, proc report, proc contents | source | 7 |
| 76 | 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, cdisc | source | 9 |
| 77 | Replay SG Graphs with IBACK in SAS/GRAPH | Abhinav Srivastva | ODS Graphics is widely used to generate high-resolution statistical graphs in a quick and flexible setting. Graph Template Language or GTL further adds to the flexibility through the means of creating templates to customize graphs. With some users looking for an alternative to GTL to combine/replay SG graphs, IBA CK in combination with GREPLAY Procedure provides an easy way to put together all SG graphs from the catalog to the desired output destination. |
proc step, proc greplay | source | 4 |
| 78 | SAS Abbreviation: The Snippets that Keep on Giving | Robert Bikwemu | When programming SAS it can seem repetitive or difficult to remember the syntax. Using SAS Abbreviations is an effective way to cut those tasks down to a few key strokes or hot-keys. This paper will show how to use SAS Abbreviations to cut those repetitive tasks out and get to the results faster. The goal is to inspire you to go review this technique and to implement SAS Abbreviations for your daily tasks. | proc step, proc transpose, sas enterprise guide | source | 4 |
| 79 | SAS Studio: A New Way to Program in SAS | Lora D. Delwiche, Susan J. Slaughter |
SAS Studio is an important new interface for SAS, designed for both traditional SAS programmers and for point-and- click users. For SAS programmers, SAS Studio offers many useful features not found in the traditional Display Manager including integrated syntax help, one touch code formatting, and the ability to drag and drop variable and data set names into your code. SAS Studio runs in a web browser. You write programs in SAS Studio, submit the programs to a SAS server, and the results are returned to your SAS Studio session. SAS Studio is included in the license for Base SAS, is the interface for SAS University Edition, and is the default interface for SAS OnDemand for Academics. Both SAS University Edition and SAS OnDemand for Academics are free of charge for non- commercial use. With SAS Studio becoming so widely available, this is a good time to learn about it. | sas studio, sas log, sas programmer, proc sql, sas library, sas enterprise guide, proc step, libname statement | source | 20 |
| 80 | 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, proc freq, create table, proc gmap, proc sql | source | 21 |
| 81 | Scripting to Automate Workflows | Chris Whalen, Amy Garrett |
SAS programmers are often required to perform repetitive tasks. Such is our lot in life. Typically these are best described as a workflow: a set of defined operations performed in fixed sequence. As programmers responsible for such deliverables, we'd sure like to automate our more mundane, repetitive work. Alas, it seems some snafu always arises in our attempts; we're reminded of that one manual step required of us to produce the deliverable, dashing our hopes for automation. Many conventionally manual steps can be automated by leveraging functionality commonly available in scripting languages. A coordinated suite of parameterized script modules able to emulate the most frequently relied upon manual operations can vastly expand the breadth of workflows that are entirely programmable, forming the basis for a reliable, highly configurable system of automation. Key features of such a system include: * Facility to load and process workflow specifications. | hash table, xml | source | 11 |
| 82 | SDTMs in a Medical Device Trial - A First Attempt | Phil Hall | While clinical trial data of pharmaceutical and medical devices have a lot in common, the differences can be difficult to deal with in SDTMs. This paper acknowledges the similarities, highlights the differences and serves as a case- study of how the Transcatheter Heart Valve (THV) Statistical Programming group of Edwards Lifesciences has decided to map their raw data into Study Data Tabulation Model (SDTM) domains. This is not intended to be the definitive guide of how to map medical device trial data to SDTM standards but to serve as the opening statement of a conversation. | clinical trial, clinical data, sdtmig, adverse events, clinical programming, cdisc | source | 5 |
| 83 | Secure Macro-Based Method to Assign LIBNAMEs for Databases | Thomas E. Billings | The use of meta-LIBNAMEs in the client-server environment is one way to securely assign LIBNAMES, i.e., the user does not know (and cannot acquire) the associated userids or passwords. Sherman & Carpenter (2009) present a 3-step SQL-based approach that does not require metadata. Their approach works with PROC SQL CONNECT TO syntax for data pulls, but does not work for assigning LIBNAMEs to relational databases. Here we describe a compiled, encrypted macro that is an enhancement and extension of their work; an approach to securely assign LIBNAMEs for relational databases. This approach uses an encrypted SAS file that contains the login parameters; the password or encryption keys and the location (path/name) of the parameter file are hidden from the user. Compiled, encrypted macros are not as secure as one might expect and methods are described to check for and mask sensitive parameters in the executable form of compiled macros. |
macro variable, proc sql, stored macro, connection string, relational database, proc step, macro definition, error message, slowly changing dimension, sample, keyword parameter, macro facility, select, macro quoting function | source | 19 |
| 84 | Sending Text Messages from SAS | Matthew T. Slaughter | Text messages (SMS) are a convenient way to receive notifications away from your computer screen. In SAS, text messages can be sent to mobile phones via the email-SMS gateway or one of a number of web APIs. This paper briefly describes various methods for sending text messages from SAS and explores possible applications. | proc step, %if-%then-%else, macro quoting function | source | 6 |
| 85 | Show Me The Job! | Richelle Serrano | The movie, Jerry Maguire, spawned several popular quotations, including "Show Me the Money!" shouted repeatedly in a phone exchange between Rod Tidwell and Jerry Maguire (played by Tom Cruise). It took Jerry Maguire almost a year to find his client (the job seeker) the best job without compromises. Finding the best job is not a short and effortless process. The paper will speak to the challenges faced by Biometrics job seekers today, and show how landing the best job, can be far less painful, and more effective, by partnering with a technical recruiter. The paper begins with what 'not' to fear in a technical resume. Statistical programming hiring managers in pharmaceutical organizations are looking for individuals with well-defined technical capabilities and a solid understanding of the clinical trial process. The paper will show what to consider when preparing your resume and what you can gain from a recruiter that understands your background and skills. | advanced sas, clinical trial | source | 4 |
| 86 | Simplifying Effective Data Transformation Via PROC TRANSPOSE | Arthur X. Li | You can store data with repeated measures for each subject, either with repeated measures in columns (one observation per subject) or with repeated measures in rows (multiple observations per subject). Transforming data between formats is a common task because different statistical procedures require different data shapes. Experienced programmers often use ARRAY processing to reshape the data, which can be challenging for novice SAS users. To avoid using complex programming techniques, you can also use the TRANSPOSE procedure to accomplish similar types of tasks. In this talk, PROC TRANSPOSE, along with its many options, will be presented through various simple and easy-to-follow examples. |
proc transpose, proc step, by group processing, repeated measures | source | 12 |
| 87 | Standardized Approaches for Analyzing Quality Improvement Interventions in Healthcare Settings | Polina Kukhareva | Healthcare institutions across United States thrive to provide high value evidence based care. To attain such care, continuous and rigorous analysis of new quality improvement interventions is important. However, interventions in the healthcare settings present many challenges to the statistical analysis, including not- normally distributed variables, missing values and zero-inflated distributions. In this paper, we describe some shortcuts for analysis of several types of health care data. More specifically, we compare using generalized linear models (GLMs) evaluated by generalized estimating equations (GEEs) against using segmented regression interrupted time series (ITS) analysis. Six handy SAS macros are included to simplify the data analysis while still accounting for the secular trends in other covariates. | proc genmod, response variable, correlation, class variable, binomial distribution, time variable, time series analysis, proc step, regression analysis, macro variable, p-value, ods output, nominal variable, interval variable, output delivery system, data science | source | 20 |
| 88 | Step Up Your Statistical Practice with Today's SAS/STAT Software | Robert N. Rodriguez | Has the rapid pace of SAS/STAT releases left you unaware of powerful enhancements that could make a difference in your work? Are you still using PROC REG rather than PROC GLMSELECT to build regression models? Do you understand how the GENMOD procedure compares with the newer GEE and HPGENSELECT procedures? When should you turn to PROC ICPHREG rather than PROC PHREG for survival modeling? This paper will increase your awareness of modern tools in SAS/STAT by providing high-level comparisons with well-established tools and explaining the benefits of enhancements and new procedures. The paper focuses on new tools in the areas of regression model building, generalized linear models, survival analysis, and mixed models. When you see the advantages of these tools, you will want to put them into practice. |
regression analysis, proc quantreg, survival analysis, proc step, proc mixed, proc phreg, binomial distribution, poisson distribution, proc reg, proc glmselect, hazard function, proc quantlife, proc genmod, data cleaning, likelihood function, logistic distribution, proc hpgenselect | source | 19 |
| 89 | Ten Things You Should Know About PROC FORMAT | Jack N. Shoemaker | The SAS system shares many features with other programming languages and reporting packages. The programming logic found in the ubiquitous DATA STEP provides the mechanisms for assignment, iteration, and logical branching which rest at the core of any procedural language. Analytic data displays, like the humble frequency cross - tabulation produced by the FREQUENCY procedure, may be replicated with varying degrees of success using any number of other products. The FORMA T procedure is another matter. Somewhat like an enumerated data type; somewhat like a normalized and indexed reference table; it really has no exact analog in these other products and pack ages. There's a l ot you can do with PROC FORMAT. And, there's a lot to know about PROC FORMAT. The aim of this paper is to provide insight on at least ten of those things which you should know. |
proc format, user-defined format, proc step, work library, proc catalog, data step, char., proc freq, proc sql | source | 10 |
| 90 | Text Wrapping with Indentation for RTF Reports | Abhinav Srivastva | It is often desired to display long text in a report field in a way to avoid splitting in an unusual manner when rendered with reporting procedures in SAS. Sometimes, the field can be a combination of several other fields or variables, in which case, the resultant text field needs to be well formatted for readability to the consumer. The paper presents a brief overview of common text wrapping and indentation options for RTF outputs and will incorporate one of the methods in building a SAS macro capable of text wrapping and indentation on one or more text fields. | missing value, proc report, macro definition | source | 6 |
| 91 | The Cox Hazard Model for Claims Data | 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 claims 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 | source | 17 |
| 92 | The Future of Transpose: How SAS Is Rebuilding its Foundation by Making What Is Old New Again | Scott Mebust | As computer technology advances, SAS continually pursues opportunities to implement state-of-the-art systems that solve problems in data preparation and analysis faster and more efficiently. In this pursuit, we have extended the TRANSPOSE procedure to operate in a distributed fashion within both Teradata and Hadoop, using dynamically generated DS2 executed by the SAS Embedded Process and within SAS Viya, using its native transpose action. With its new ability to work within these environments, PROC TRANSPOSE provides you with access to its parallel processing power and produces results that are compatible with your existing SAS programs. |
proc transpose, proc step, sas log, by group processing, cas, sas viya, hash table, options statement, casl, parallel processing, proc sql | source | 16 |
| 93 | The Role of A Data Consultant at Kaiser Permanente | Cyrus Davis | The purpose of this presentation is to provide an insider's perspective on my role as a data consultant for Kaiser Permanente (KP). I will start with the scope of SAS use within KP. I will discuss the integral role that SQL plays in accessing KP's electronic medical record known as: HealthConnect. Finally, I will provide some strategy tips in building consensus for your data. | proc sql, proc step | source | 6 |
| 94 | Using SAS Enterprise Miner for Categorization of Customer Comments to Improve Services at the U.S. Postal Service | Olayemi Olatunji | Delivering high-quality service and providing excellent customer experiences are performance outcome goals the U.S. Postal Service has established to measure corporate strategy success and continuous improvement efforts. Social media has opened the door for customer engagement and decision making. With the help of Twitter, Facebook, and Yelp, government agencies are more informed about how customers feel about their service and experience. Using Yelp data we will text mine comments about U.S. Postal Service customer service, retail service, mail delivery, and facility services using SAS Text Miner of SAS Enterprise Miner 7.1. The aim of this paper is to provide ways to categorize consumer comments regarding U.S. Postal Service services to improve the customer experiences at stations. | parsing, data mining | source | 7 |
| 95 | Using SAS to Proactively Monitor Drug Alerts: Implementing the URAC "Drug-Drug Interactions" Measure for a Specialty Pharmacy | Karen E. Wallace | To satisfy annual URAC accreditation requirements, one of the mandatory reported "Safe Care" domain measures is "Drug-Drug Interactions" (DM2012-13). This measure relies upon pharmacy and enrollment data to assess the percentage of patients who: 1) received a prescription for a target medication during the measurement period, and 2) were dispensed a concurrent prescription for a precipitant medication. The resultant data are stratified by line of business (URAC, 2016). Using SAS Base programming, in conjunction with technical and clinical review, the preliminary methodology: 1) imports and joins several files using the data elements of patient identifier as well as medication and insurance plan, 2) cleanses the mixed-case, punctuated MediSpan-generated drug alert text, 3) categorizes the text into discrete flags based upon predetermined criteria, 4) sums the number of flags per observation, and finally, 5) segments data by insurance type. | medical history, excel | source | 8 |
| 96 | Validation Method Selection, Not Just a 'Match' | Rituraj Saxena, Jeanina M. Worden |
Quality Control (QC) and Quality Assurance (QA) are an integral part of the Clinical trial process and are required both to maintain standards and assure excellence in successfully completed trials. The main difference between QC and QA is: QC is a validation process to identify abnormalities and inconsistencies with outputs and correct them while QA is a preventative process to keep abnormalities and inconsistencies from occurring and remaining undetected. QA is a department -level activity (i.e. Standard Operating Procedures (SOPs) and Work Instructions (WIs)) and QC is a task -level activity. The focus of this paper is primarily on QC. In the Pharmaceutical/biotech industry validation requirements have progressed significantly since FDA published Title 21 Code of Federal Regulations Part 11 (21 CFR Part 11). | data cleaning, proc compare, proc step | source | 9 |
| 97 | Variance Ratio Estimation Using the PRECMOD SAS Macro | Jesse Albert Canchola, Pari Hemyari |
When comparing two groups, the first thing that comes to the analyst's mind is to compare means. However, it may become necessary to compare group dispersions such as the variance, between the two groups to show equivalence, especially in a regulatory setting. The user is taken through the much vaunted PrecMod SAS macro to show how this can be done easily using the SAS System. | degrees of freedom, sample, confidence interval, macro definition, probability distribution, significance level, standard deviation, macro parameter, sample size, proc report, proc mixed | source | 12 |
| 98 | Which Paint Brush Shall I Choose -- Mapping Out Your Visualization Plan for Clinical Studies | Chen Shi | During a clinical study, there are a lot of aspects that we want to see and investigate. Often throughout the process, the visualization needs involve multiple functions including medical affairs, pharmacovigilance, regulatory, publishing, data management and clinical operations etc. Each function has their own way of displaying and understanding the | clinical trial, data science | source | 5 |
| 99 | Working with the SAS ODS EXCEL Destination to Send Graphs, and Use Cascading Style Sheets When Writing to EXCEL Workbooks | William E. Benjamin Jr. | This Hands-On-Workshop will explore the new SAS ODS EXCEL destination and focus on how to write EXCEL Worksheets with output from SAS Graph procedures and spice it up using Cascading Style Sheet features available on modern computer systems. Note that the ODS EXCEL destination is a BASE SAS product, which makes it available on all platforms. The workshop will be limited to the Windows platform, but it should be simple to port the code to other operating systems. The code will be on the computers and you will get a chance to see how it handles. |
excel, xls, output delivery system, sas base, proc template, proc step, cdisc | source | 19 |
| 100 | Your Local Fire Engine Has an Apparatus Inventory Sheet and So Should Your Software: Automatically Generating Software Use and Reuse Libraries and Catalogs from Standardized SAS Code | Troy Martin Hughes | Fire and rescue services are required to maintain inventory sheets that describe the specific tools, devices, and other equipment located on each emergency vehicle. From the location of fire extinguisher s to the make, model, and last service date of power tools, inventory sheets ensure that firefighters and rescue personnel know exactly where to find equipment during an emergency , when restocking an apparatus, or when auditing an apparatus ' inventory. At the department level, inventory sheets can also facilitate immediate identification of equipment in the event of a product recall or the need to upgrade equipment. Software should be similarly monitored within a production environment, first and foremost to describe and organize code modules-often SAS macros-so they can be discovered and located when needed. | sas enterprise guide, sas enterprise guide project, macro variable, macro definition, sample, proc report, filename statement, variable | source | 19 |
| 101 | Zeroing In on Effective Member Communication: An Rx Education Study | Brian Mitchell | In 2013, the Centers for Medicare & Medicaid Services (CMS) changed the pharmacy mail-order education process so that Humana Pharmacy may only call a member with cost savings to educate the member on the potential savings and instruct the member to call back. The Rx Education call center asked for analytics work to help prioritize member outreach, improve education success, and decrease the number of members who are unable to be contacted. After a year of contacting members using this additional model insight, the education rate rose significantly and the unable to contact rate fell by more than half; this case study takes you on an analytics journey from the initial problem diagnosis and analytics solution, followed by refinements, as well as test and learn campaigns. | sample, proc surveyselect | source | 4 |
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