Nothing like a good snowstorm to put you in your place. And make you appreciate the Wi-Fi access to work since the roads near your house won’t be fully plowed for a few days. Yes, welcome to winter precipitation in the southern US! But last week’s snow was better than ice, and SAS can handle tons of remote access, so progress was made.

We announced yet another release of SAS^® software before the holidays, and it was a big one. The big release of SAS^® Viya^® products includes SAS^® Visual Statistics, SAS^® Data Mining and Machine Learning, SAS^® Econometrics, and SAS^® Optimization. And if you also have SAS^® 9.4M5 installed, you can work with both from the same SAS interface, such as SAS^® Enterprise Guide^®, SAS^® Studio, and the SAS windowing environment. Learn about what we offer in both SAS 9.4M5 and SAS Viya.

Another great way to catch up with our new software directions is to attend SAS^® Global Forum in Denver, April 8–11. 

Early-bird registration is available through March 5, and the conference provides a host of analytical talks as well as dozens of short presentations on the exhibition floor and opportunities to talk shop with SAS developers. In addition, we offer a bunch of pre-conference tutorials that include presentations on mixed models, causal analysis, survey data analysis, and longitudinal data analysis. You can advance your ODS Graphics skills or learn all about modern machine learning techniques with SAS^® Visual Data Mining and Machine Learning software. And if you’ve already registered, it’s easy enough to add one of these courses now! 

Here’s to better weather, wherever you are!

Maura Stokes

Senior R&D Director, Statistical Applications

Technical Papers

Evaluating Predictive Accuracy of Survival Models with PROC PHREG

Model validation is an important step in the model building process because it provides opportunities to assess the reliability of models before their deployment. Predictive accuracy measures the ability of the models to predict future risks, and significant developments have been made in recent years in the evaluation of survival models. SAS/STAT 14.2 includes updates to the PHREG procedure with a variety of techniques to calculate overall concordance statistics and time-dependent receiver operator characteristic (ROC) curves for right-censored data. This paper describes how to use these criteria to validate and compare fitted survival models and presents examples to illustrate these applications.

Propensity Score Methods for Causal Inference with the PSMATCH Procedure

To establish causal interpretations of the treatment effects in observational studies, special statistical approaches that adjust for the covariate confounding are required in order 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. Prior to the analysis, you use propensity scores to adjust the data by weighting observations, stratifying subjects that have similar propensity scores, or matching treated subjects to control subjects. This paper reviews propensity score methods for causal inference and introduces the PSMATCH procedure, which was new in SAS/STAT 14.2. The procedure provides methods of weighting, stratification, and matching. Matching methods include greedy matching, matching with replacement, and optimal matching. The procedure assesses covariate balance by comparing distributions between the adjusted treated and control groups.

Solving Business Problems with SAS Analytics and PROC OPTMODEL

View these slides from the spring 2017 INFORMS conference to learn about optimization problems that you can solve with SAS/OR^® software.

Technical Highlights

May I Direct Your Attention To:

The CUSTOM statement in PROC POWER in SAS/STAT^® performs power and sample size analyses for extensions of existing analyses that involve the chi-square, F, t, normal, or correlation coefficient distribution. Use cases include logistic regression with classification variables, Poisson regression, zero-inflated models, and other generalized linear models.

The HPGENSELECT procedure in SAS/STAT performs model building for generalized linear models. It provides the LASSO method. Learn more. 

Tech Support Points Out

Talks and Tutorials

Check out the many machine learning–related talks coming up at SAS Global Forum this
spring. 

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