Upcoming Events:

Summer Webinar

Causal Analysis Using SAS Statistics Procedures
This talk overviews some recently developed SAS procedures that provide statistical tools for causal analysis, including causal effect estimation, causal mediation analysis, and causal graph theory for establishing valid estimation strategies. After basic principles of causal analysis are summarized, important features of the causal procedures are demonstrated through examples.

Presented by Clay Thompson. Clay Thompson is a senior research statistician developer in Advanced Analytics R&D at SAS Institute, where he supports methods for causal analysis and sequential trials. He has taught workshops and CE courses on causal analysis at JSM and the International Chinese Statistical Association. Prior to joining SAS, he worked as a quantitative systems pharmacologist at Pfizer Inc. He received his PhD in applied mathematics from NCSU.

Analyzing observational data with the PSMATCH procedure
Learn about how you can use the PSMATCH procedure and propensity-score-based methods to support the estimation of causal effects from nonrandomized data. This talk reviews the definition of causal effects in a counter factual framework and provides examples of how you can use propensity score based matching and inverse probability weighting to adjust for confounding variables. No prior experience with the material is required.

Presented by Michael Lamm, a senior research statistician developer in the Advanced Analytics R&D at SAS Institute. Among his responsibilities are developing software in causal analysis and statistical learning. He has taught courses on causal analysis at conferences including JSM, and the ENAR Spring Meetings. He received his PhD in statistics and operations research from UNC Chapel Hill.

Past Events:

Winter Webinar

  • Date: December 17, 2020
  • Time: 10:00 a.m. – 12:00 p.m. EST
  • Location/Format: Webinar link

1. Causal Effect Estimands: Interpretation, Identification, and Computation (slides)
In modern statistics and data science, there is growing attention on estimating causal effects by using data from nonrandomized or imperfectly randomized studies. This task arises in applications such as post-approval analysis of medical treatments, evaluation of public policies, and assessment of marketing campaign efficacy. One challenge of these applications is the variety of causal effects that you can estimate. For example, you might need to determine whether to estimate the average treatment effect (ATE), the average treatment effect for the treated (ATT), a mediated effect, or other conditional effects. Identifying the causal effect most relevant to your application can have important implications for determining what approach to causal inference is most appropriate. This presentation provides an overview of different types of causal estimands, a comparison of how the different estimands are interpreted, and guidance on how identifying an appropriate estimand can help you determine an appropriate causal analysis workflow. The CAUSALGRAPH, CAUSALMED, CAUSALTRT, and PSMATCH procedures in SAS/STAT® software are used to demonstrate the workflow. The presentation also includes a review of the assumptions that are required for identifying and estimating causal effects.

Presented by Clay Thompson. Clay Thompson is a Senior Research Statistician Developer in the Multivariate Models Research Department at the SAS Institute, where (among other things) he develops algorithms and software for the analysis of causal effects using graphical models. His career has focused on research problems at the intersection of computational science, mathematics, and health sciences. Prior to SAS, he worked as a quantitative systems pharmacologist in the pharmaceutical industry. He received a PhD in Applied Mathematics from North Carolina State University.

2. PROC SQL vs. DATA Step Programming (slides)
Everyone wants to know: Should I use the DATA step or PROC SQL to join this data? Take a behind the scenes look at how the DATA step and SQL procedure process data by comparing all types of joins (inner, left/right, outer) with multiple types of data (one-to-one, one-to-many, many-to-many).

Presented by Mary-Elizabeth Eddlestone, Analytics Technical Advisor, SAS Customer Success Organization
Demystifying analytics has been a career-long quest for Mary-Elizabeth (“M-E”) Eddlestone, an Analytics Technical Advisor on the SAS Customer Success team. Having studied Economics and Quantitative Methods at Mount Holyoke College and Cornell University, M-E has used SAS analytics to study, model, forecast, and predict a wide range of subjects in a variety of industries. M-E began programming in SAS as an undergraduate and has used SAS in every job since. She has spent the last several years at SAS helping customers discover the power of SAS analytics and has presented at, and served as section chair for SUGI/SAS Global Forum, Analytics, as well as several regional, local and in-house SAS user groups.
Certification: Predictive Modeler Using SAS® Enterprise Miner™

Handling Missing Data in SAS and Proven Practices for Predictive Modeling

  • Date: August 6, 2020
  • Time: 11:00 a.m. – 1:00 p.m. EST
  • Location/Format: Webinar link

1. Handling Missing Data in SAS (slides/video)
What do you do when you have missing values in your data? In SAS we have many ways to manage missing values. In this session we cover what are missing values, why and when missing values occur and how to manage missing values. We discuss functions, procedures and how different products deal with missing values.

2. Proven Practices for Predictive Modeling(slides/video)
In our ongoing quest for “analytics excellence,” what are some of the strategies and tactics that we, as analytics practitioners, can consider not only for individual predictive modeling projects, but for increasing the value and importance of analytics in our organizations? This presentation will share some of the common strategies, attributes, processes and best practices of the most successful organizations. Best practices will include considerations for an overall analytics process as well as the discrete steps of building a predictive model, such as data preparation and sampling; input (variable) examination, selection and transformation; model selection and validation; and more.

Melodie Rush is a Principal Data Scientist for the SAS Global Customer Success Technical Team. Melodie received both her B.S. in Statistics and her Masters in Science of Management from North Carolina State University. Before joining SAS, Melodie worked for Research Triangle Institute as a Statistician. Her responsibilities included implementing national and local surveys of various topics, such as health care, employee benefits, and drug abuse. As part of her research, she has published work for both the American Statistical Association and the American Public Health Association. After joining SAS, Melodie has developed presentations and methodology for doing many types of analysis, including data mining, machine learning, forecasting, data exploration and visualization, quality control and marketing. She has spent the last 20+ years helping companies identify and solve problems in each of these analytical areas.

Date & Time: Thursday, December 5, 2019 8:15 AM-12:30 PM

  • Presentation 1: “Free Online SAS® Resources”
  • Presentation 2: “A Tour of the SAS University Edition (AKA “Free SAS”)”
  • Presentation 3: “A Survey of Some of the Most Useful SAS Functions”
  • Presentation 4: “Panel Discussion: Advice for Students and Early Career SAS Professionals”
  • BASUG Training Session

    Using JupyterLab with SAS and Tips for SAS EG

    • Date: Thursday, November 14, 2019
    • Time: 11:00 a.m. – 12:00 p.m.
    • Location/Format: Webinar

    1. Learning Data Science with SAS® University Edition and JupyterLab (Brian Gaines)

    One of the interfaces included with SAS® University Edition is the popular JupyterLab interface. You can use this open-source interface to generate dynamic notebooks that easily incorporate SAS® code and results into documents such as course materials and analytical reports. The ability to seamlessly interweave code, results, narrative text, and mathematical formulas all into one document provides students with practical experience in creating reports and effectively communicating results. In addition, the use of an executable document facilitates collaboration and promotes reproducible research and analyses. After a brief overview of SAS University Edition, this paper describes JupyterLab, discusses examples of using it to learn data science with SAS, and provides tips. SAS University Edition is available at no charge to educators and learners for academic, noncommercial use and includes SAS® Studio, Base SAS®, SAS/STAT®, SAS/IML®, and some other analytical capabilities.

    2.  20 in 20: Quick Tips for SAS® Enterprise Guide® Users (Kelly Gray)

    There are many time-saving and headache-saving tips and tricks you can use to make working in SAS® Enterprise Guide® a breeze. Did you know that you can change your layout so that you can see your code and your results at the same time? You will learn 20 tips and tricks for working in SAS Enterprise Guide in 20 minutes. One tip per minute, and out of the twenty you are guaranteed to find at least one nugget that will making your life easier.

    SAS Blowout Event

    Co-hosted by Boston Area SAS® Users Group and SAS Institute Inc.

    • Wednesday, September 18, 2019
    • 8:15 a.m. – 4:30 p.m.
    • The NonProfit Center, developed by TSNE MissionWorks
    • 89 South Street, Boston, MA 02111

    BASUG invites you to join us for our 6th Annual SAS Blowout, featuring presentations by three senior SAS Institute developers. Come for all or part of the day, and be sure to stay for our make- your-own sundae social event, where you can schmooze with your colleagues and this incredible team of presenters from SAS.

    Event agenda

    DASUG Annual General Meeting

    Join fellow SAS users and SAS experts for the DASUG meeting on Thursday, July 11, at Dartmouth College. You can network with local SAS users, boost your skills, and learn about some of the latest SAS tools and technologies.

    Presentations by SAS

    DASUG Webinar Information:

    Register now

    Lisa Horwitz from SAS will present:

    Change is Good, or at Least Expected: Techniques for Visualizing Categorical Values Over Time

    Date and time: Friday, December 7, 2018 2:00 pm (Eastern Standard Time)

    Duration: 0.5 hour


    Many techniques exist for showing how numeric values change over time. Bar charts, line charts, plots and many other graph types are all excellent ways to demonstrate how temperature, expenses and other measures increase and decrease over minutes, months or decades. On the other hand, such graphs don’t lend themselves to showing how and when categorical values such as grade, rating, score and status change over time. A simple combination of data manipulation, file merging, custom formatting and the Output Delivery System (ODS) can produce a wide range of useful, easy-to-interpret and effective reports. By using color, fonts, custom messages and other features to indicate a change in a data value, these reports make it easy to monitor progress or to detect when things are going in the wrong direction. SASGF18


    Ray Wright from SAS will present:

    Interpreting Black-Box Machine Learning Models Using Partial Dependence and Individual Conditional Expectation Plots

    Date and time: Friday, December 7, 2018 2:30 pm (Eastern Standard Time)

    Duration: 0.5 hour


    One of the key questions a data scientist asks when interpreting a predictive model is “How do the model inputs work?” Variable importance rankings are helpful for identifying the strongest drivers, but these rankings provide no insight into the functional relationship between the drivers and the model’s predictions.

    Partial dependence (PD) and individual conditional expectation (ICE) plots are visual, model-agnostic techniques that depict the functional relationships between one or more input variables and the predictions of a black-box model. For example, a PD plot can show whether estimated car price increases linearly with horsepower or whether the relationship is another type, such as a step function, curvilinear, and so on. ICE plots enable data scientists to drill much deeper to explore individual differences and identify subgroups and interactions between model inputs.

    This paper shows how PD and ICE plots can be used to gain insight from and compare machine learning models, particularly so-called “black-box” algorithms such as random forest, neural network, and gradient boosting. It also discusses limitations of PD plots and offers recommendations about how to generate scalable plots for big data. The paper includes SAS® code for both types of plots. SASGF18

    DASUG invites you to join this Webex meeting.
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    Friday, December 7, 2018
    2:00 pm | Eastern Standard Time (New York, GMT-05:00) | 1 hr
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    Meeting information

    Featured presentations

    • Hidden Gems in SAS® Enterprise Guide® – Kate Schwarz, Senior Systems Engineer, Customer Success Technical Team
    • Propensity Score Methods for Causal Inference With the PSMATCH Procedure – Maura Stokes, Senior Director, Advanced Analytics R&D
    • SAS/STAT® 14.3 Roundup: Modern Methods for the Modern Statistician – Maura Stokes, Senior Director, Advanced Analytics R&D


    Webinar information

    Lisa Horwitz from SAS will present:

    A Long-Time SAS® Programmer Learns New Tricks

    Date and time: Friday, October 20, 2017 2:00 pm (Eastern Standard Time)

    Duration:1 hour

    When it’s time, join the meeting.

    Meeting number: 595 279 717 Meeting password: GMmSr382

    Join by phone

    +1 866 282 7366 US Toll Free

    +1 210 606 9466 US Toll


    When a large and important project with a strict deadline hits your desk, it’s easy to revert to those tried-and-true SAS programming techniques that have been successful for you in the past. In fact, trying to learn new techniques at such a time may prove to be distracting and a waste of precious time. However, the lull after a project’s completion is the perfect time to reassess your approach and see if there are any new features added to the SAS arsenal since the last time you looked that could be of great use the next time around. Such a post-project post-mortem has provided me with the opportunity to learn about several new features which will prove to be hugely valuable as I rework this project for Round 2:

    • The presenv option and procedure
    • Fuzzy matching with the compged function
    • The ODS powerpoint statement
    • SAS Enterprise Guide enhancements including copying and pasting process flows and the macro variable viewer


    Meeting information

    Featured presentations


    Webinar information

    Date and time: Friday, November 18, 2016 2:00 pm
    Eastern Standard Time (New York, GMT-05:00)
    Friday, November 18, 2016 1:00 pm
    Central Standard Time (Chicago, GMT-06:00)
    Friday, November 18, 2016 11:00 am
    Pacific Standard Time (San Francisco, GMT-08:00)
    Duration: 1 hour
    Gordon G. Brown from SAS will present:
    Encore: Introduction to Bayesian Analysis Using SAS/STAT®
    The use of Bayesian methods has become increasingly popular in modern statistical analysis, with applications in numerous scientific fields. In recent releases,SAS® has provided a wealth of tools for Bayesian analysis, with convenient access through several popular procedures,as well as the MCMC procedure, which is designed for general Bayesian modeling.This paper introduces the principles of Bayesian inference and reviews the steps in a Bayesian analysis.It then describes the built-in Bayesian capabilities provided in SAS/STAT, which became available for all platforms with SAS/STAT 9.3,with examples from the GENMOD and PHREG procedures. How to specify prior distributions,evaluate convergence diagnostics and interpret the posterior summary statistics are discussed.

     Analyzing Multilevel Models with the GLIMMIX Procedure by Phil Gibbs. Thursday, July. 16, 2015 1:00 – 2:00 p.m.Learn how to use the GLIMMIX procedure in SAS/STAT to analyze hierarchical data that have a wide variety of distributions. Examples are included to illustrate the flexibility that PROC GLIMMIX offers for modeling within-unit correlation, disentangling explanatory variables at different levels, and handling unbalanced data.

    A_First_Look_at_the_ODS_Destination_for_PowerPoint by Tim Hunter. Thursday, July. 16, 2015 2:15 – 3:15 p.m.This presentation introduces the ODS destination for PowerPoint, one of the next generation of ODS destinations.

    Introduction to the SAS® Macro Facility by T. Winand. Friday, Jan. 30, 2015 2:30 – 3:30 p.m.Learn about the macro facility in an introduction to its purpose and functionality. You will learn the basics of creating and using both macro variables and macro programs.

    Top 10 Ways to Optimize Your SAS code by T. Winand. Friday, Jan. 30, 2015 3:40 – 4:40 p.m.Learn tips and tricks to make your SAS code run more efficiently. There are at least six ways to do most things in SAS, so understanding some coding guidelines can help to guide efficient decisions. Some tips include: limiting the amount of data read, index usage, and efficient conditional processing.

    STDRATE procedure and the EFFECT statement in SAS/STAT® by Phil Gibbs. Friday, Jan. 31 1:30 – 4 p.m.

    Thursday, June 27 9:30 – 12:30

    • An Introduction to Creating Multisheet Microsoft Excel Workbooks the Easy Way with SAS” with Vince DelGobbo

    Vince DelGobbo’s ExcelXP Tagset Paper IndexWednesday, March 27 2013 12:00pm

    Tuesday, October 9, 2012 4:00pm

    Wednesday, March 21, 2012 4:00pm

    Wednesday Oct. 12, 2011 12-1:30pm

    June 8th, 2011 12:00pm-1:30pm Webinar:

    March 17th, 2011 4:00-6:00pm @ 35 Centerra Parkway, Lebanon, NH (TDI, Dartmouth)

    Oct. 7th, 2010 @ Lebanon, NH (TDI, Dartmouth)

    June 23 DASUG: Health Care Claims Data Symposium 9am to 5pm @ Lebanon, NH

    April 29 from 4-6pm@ Lebanon, NH

    November 5, 2009 @ Lebanon, NH

    June 11th, 2009 DASUG Officer’s meeting

    April 9th, 2009 @ Lebanon, NH (TDI, Dartmouth)

    Oct. 21, 2008 @ Dartmouth College

    July 10, 2008

    April 10, 2008

    September 14, 2007

    • Maura Stokes (SAS Institute) presented “Applications of GEE Methodology Using the SAS System “.
    • Meeting Minutes

    June 12, 2007

    March 15, 2007

    • Foster Kerrison presented “Hey, let SAS do the work!”. Click here to download the sample SAS code and presentation slides.
    • Meeting Minutes

    October 31, 2006

    • Rebecca Symes, Maine Health Information Center, talked about “The Use of DDE in Exporting Data to Excel”. Click here to download the sample SAS code, SAS data and Excel File.
    • Meeting Minutes

    January 26, 2006

    December 10, 2004

    September 17, 2004

    June 18, 2004

    March 19, 2004

    December 19, 2003

    September 19, 2003

    June 20, 2003

    March 21, 2003

    December 13, 2002

    September 26, 2002

    June 27, 2002

    March 18, 2002

    December 14, 2001

    • Paul Grant, from the SAS Institute, lead a very informative discussion on accessing data through various methods. (ODBC, OLE DB, DDE, Proc Import, Proc Export, etc.)
    • Meeting Minutes

    September 20, 2001

    • Judy Loren from LLBean presented a talk on SQL
    • Foster Kerrison presented a problem regarding a delimited file with missing values, along with several other SAS coding questions
    • Meeting Minutes

    June 01, 2001

    • Mike Zdeb from New York presented “Creating Maps with SAS/GRAPH” and “Beyond Format Basics”.
    • Meeting Minutes

    January 22, 2001

    • Jim Conley, from Elliot Hospital , discussed the SAS Certification Exam Process
    • Coding Workshop covered topics on merging data sets, LIBNAME, FILENAME, and FILEVAR
    • Meeting Minutes

    November 10, 2000

    • Liza Horwitz of the SAS Institute presented a paper titled “Getting Started with SAS or What the Data Step Can Do For You!”
    • Candy Riel and Pat Kelly gave a presentation from the users perspective of the enhancements within Version 8.0
    • Meeting Minutes

    April 27, 2000

    Wednesday, March 21, 2012 4:00pm

    Programming Techniques for Optimizing SAS Throughput by Ruegsegger of IBM Microelectronics in Essex Junction, VT

    Dartmouth Area SAS® Users Group

    Join your colleagues and SAS experts to network with local SAS users, boost your skills and learn about some of the latest SAS tools and technologies.

    Featured presentations

    Meeting information

    • July 15
    • 12:30 p.m. – Lunch/Networking
    • 1 – 4:30 p.m. – Presentations
    • Dartmouth College
      Collis 101
      6181 Collis Center
      Hanover, NH 03755
    An Insider’s Guide to ODS LAYOUT Using SAS® 9.4
    Friday, November 20, 2015
    2:00 pm | Eastern Standard Time (New York, GMT-05:00) | 1 hour