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I’m recently back from a weeklong course on causal inference at the Harvard T. H. Chan School of Public Health in Boston, Massachusetts. It was great to sit and learn from the best in this area, one to which SAS has contributed lately with the PSMATCH and CAUSALTRT procedures, with more to come. It was interesting to hear about the data that had spurred methodological advances as well as the problems yet to be solved. (Yes to those who know me: I did manage to get to Fenway a couple of nights—both extra innings, both wins!)

We are heading off to teach at the Joint Statistical Meetings in Baltimore, July 29 through August 3, with two-hour tutorials in analyzing multilevel models with PROC GLIMMIX, causal treatment effect analysis, advanced methods for survival analysis, and weighted GEE analysis. It’s not too late to add these tutorials to your registration. And please come see us on the exhibition floor, where we will be talking about recent and upcoming additions to SAS/STAT® and SAS/ETS® software as well as demoing our latest venture into machine learning on the SAS® Viya™ platform.

Kudos to the folks who coordinated the Michigan Users Group day a couple of weeks ago. The event attracted over 300 attendees, and I had a great audience for my tutorial on analytical categorical outcomes in longitudinal data analysis as well as many interesting conversations with users throughout the day. The organizers ran two tracks, and it was an impressive effort all around.

Here’s to the summer.  

Maura Stokes

Senior R&D Director, Statistical Applications 


Technical Highlights

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The DO Loop

Ever wonder why you might need the OPTEX procedure in SAS/QC® software? Distinguished Research Statistician Developer Rick Wicklin explains the need and illustrates the application of PROC OPTEX with a straightforward example

Wicklin also discusses quantile definitions in SAS® and discusses modern ways of performing regression with restricted cubic splines by using the EFFECT statement


SAS/STAT User Still Moving to the SAS® 9.4 Platform? 

If you are moving up to SAS 9.4 and would like to catch up on the recent SAS/STAT releases on that platform, this handout is for you! Get an overview of our new additions in missing data analysis, modern survival data analysis, statistical modeling, spatial point pattern analysis, Bayesian analysis, item response analysis, classification and regression trees, and performance enhancements. There’s truly something here for everyone. And if you aren’t currently on the move, feel free to use this handout however it helps you get into the passing lane! 


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Graphically Speaking 

We always talk about how SAS users drive what we do, from software direction to the shape of documentation to conference content. Distinguished Research Statistician Developer Warren Kuhfeld has written several blog posts that were prompted by interactions with customers. A post about how to approach creating a new graph resulted from a user’s question about combining two different types of graphs. And Kuhfeld wrote about how to use GTL expressions after another user inquired about how to suppress part of the correlation loading plot in the PLS procedure. Keep asking those questions! 


Technical Papers


Big Value from Big Data: SAS/ETS Methods for Spatial Econometric Modeling in the Era of Big Data

Data that are gathered in modern data collection processes are often large and contain geographic information that enables you to examine how spatial proximity affects the outcome of interest. For example, in real estate economics, the price of a housing unit is likely to depend on the prices of housing units in the same neighborhood or nearby neighborhoods, either because of their locations or because of some unobserved characteristics that these neighborhoods share. Understanding spatial relationships and being able to represent them in a compact form are vital to extracting value from big data. This paper describes how to glean analytical insights from big data and discover their big value by using spatial econometric methods in SAS/ETS software.


Writing Packages: A New Way to Distribute and Use SAS/IML® Programs

SAS/IML 14.1 enables you to author, install, and call packages. A package consists of SAS/IML source code, documentation, data sets, and sample programs. Packages provide a simple way to share SAS/IML functions. An expert who writes a statistical analysis in SAS/IML can create a package and upload it to the SAS/IML File Exchange. A nonexpert can download the package, install it, and immediately start using it. Packages provide a standard and uniform mechanism for sharing programs, which benefits both experts and nonexperts. Packages are very popular with users of other statistical software, such as R. This paper describes how SAS/IML programmers can construct, upload, download, and install packages.


Customizing the Kaplan-Meier Survival Plot

You might not know that the SAS/STAT documentation includes an entire chapter on how to customize the Kaplan-Meier plot. This plot, which displays survival (time-to-event) data, is probably one of the most popular statistical plots in play. Learn how to customize it using ODS Graphics so that it displays exactly what you want and how you want to do it. For more information about this documentation, see Distinguished Research Statistician Developer Warren Kuhfeld’s blog post


Estimating Causal Effects from Observational Data with the CAUSALTRT Procedure

Randomized control trials have long been considered the gold standard for establishing causal treatment effects. Can causal effects be reasonably estimated from observational data too? In observational studies, you observe treatment T and outcome Y without controlling confounding variables that might explain the observed associations between T and Y. Estimating the causal effect of treatment T therefore requires adjustments that remove the effects of the confounding variables. The new CAUSALTRT (causal-treat) procedure in SAS/STAT 14.2 enables you to estimate the causal effect of a treatment decision by modeling either the treatment assignment T or the outcome Y, or both. Specifically, modeling the treatment leads to inverse probability weighting methods, and modeling the outcome leads to regression methods. Combined modeling of the treatment and outcome leads to doubly robust methods that can provide unbiased estimates for the treatment effect even if one of the models is misspecified. This paper reviews the statistical methods that are implemented in the CAUSALTRT procedure and includes examples of how you can use this procedure to estimate causal effects from observational data. The paper also illustrates some other important features of the CAUSALTRT procedure, including bootstrap resampling, covariate balance diagnostics, and statistical graphics.


Tech Support Points Out

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Using the FASTQUAD Suboption in PROC GLIMMIX to Overcome Long Computing Times or Insufficient Memory Problems

One of the most expensive pieces of estimating a generalized linear mixed model is evaluating the likelihood function. There are six different methods for taking on this calculation in PROC GLIMMIX, from pseudo-likelihood evaluation to estimating the exact likelihood. When choosing from these methods, you need to consider the effects of estimation bias with certain response types. You also need to weigh the cost of the likelihood evaluation. Some of these methods require more memory and run time than others. Beginning with SAS/STAT 14.1 in SAS 9.4 TS11M3, the FASTQUAD quad-option for the METHOD=QUAD option can often alleviate long run times and memory constraints. FASTQUAD uses the multilevel adaptive quadrature algorithm proposed by Pinheiro and Chao (2006). 


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