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 in there for everyone. And if you aren't currently on the move, feel free to use this handout however it helps you to get into the passing lane!

Contemporary data collection processes usually involve recording information about the geographic location of each observation. This geospatial information provides modelers with opportunities to examine how the interaction of observations affects the outcome of interest. For example, it is likely that car sales from one auto dealership might depend on sales from a nearby dealership, either because the two dealerships compete for the same customers or because some form of unobserved heterogeneity is common to both dealerships. Knowledge of the size and magnitude of the positive or negative "spillover" effect is important for creating pricing or promotional policies. This paper describes how geospatial methods are implemented in SAS/ETS and illustrates some ways you can incorporate spatial data into your modeling toolkit.

Distinguished Research Statistician Developer Warren F. Kuhfeld has written a new book called Basic ODS Graphics Examples. Available as a free PDF file on the web, the book is in color, and you can access all the SAS code by double-clicking a link at the beginning of each example. This book complements Kuhfeld's other recent work,  Advanced ODS Graphics Examples , and replaces his 2010 SAS Press book, Statistical Graphics in SAS: An Introduction to the Graph Template Language and the Statistical Graphics Procedures. Like the 2010 book, the new basic book provides a gentle and parallel introduction to the Graph Template Language and the SG procedures. Many new examples have been added.

The popular MIXED, GLIMMIX, and NLMIXED procedures in SAS/STAT software fit linear, generalized linear, and nonlinear mixed models, respectively. These procedures take the classical approach of maximizing the likelihood function to estimate model parameters. The flexible MCMC procedure in SAS/STAT can fit these same models by taking a Bayesian approach. Instead of maximizing the likelihood function, PROC MCMC draws samples (using a variety of sampling algorithms) to approximate the posterior distributions of model parameters. Like the mixed modeling procedures, PROC MCMC provides estimation, inference, and prediction. This paper describes how to use the MCMC procedure to fit Bayesian mixed models and compares the Bayesian approach to how the classical models would be fit using the familiar mixed modeling procedures. Several examples illustrate the approach in practice.

Survey data commonly include missing values due to nonresponse. You can impute the missing values by replacing them with reasonable nonmissing values. In addition to performing traditional cell-based hot-deck imputation, the SURVEYIMPUTE procedure, new in SAS/STAT 14.1, performs modern fully efficient fractional imputation (FEFI). FEFI is a variation of hot-deck imputation in which all potential donors in a cell contribute their values. The ability to fill in missing values is only one feature of PROC SURVEYIMPUTE. The real trick is that it can perform analyses of the filled-in data that appropriately account for the imputation. PROC SURVEYIMPUTE also creates a set of replicate weights that are adjusted for FEFI. Thus, if you use the imputed data from PROC SURVEYIMPUTE along with the replicate methods in any of the survey analysis procedures—SURVEYMEANS, SURVEYFREQ, SURVEYREG, SURVEYLOGISTIC, or SURVEYPHREG—you can be confident that inferences account not only for the survey design but also for the imputation.

This paper discusses different approaches for handling nonresponse in surveys, introduces PROC SURVEYIMPUTE, and demonstrates its use with real-world applications.

Statistical quality improvement is based on understanding process variation, which falls into two categories: variation that is natural and inherent to a process, and unusual variation due to specific causes that can be addressed. If you can distinguish between natural and unusual variation, you can take action to fix a broken process and avoid disrupting a stable process. A control chart is a tool that enables you to distinguish between the two types of variation. In many health care activities, carefully designed processes are in place to reduce variation and limit adverse events. The types of traditional control charts that are designed to monitor defect counts are not applicable to monitoring rare events, because these charts tend to be overly sensitive, signaling unusual variation each time an event occurs. In contrast, specialized rare events charts are well suited to monitoring low-probability events. These charts have gained acceptance in health care quality improvement applications because of their ease of use and their suitability for processes that have low defect rates. This paper presents an overview of the RAREEVENTS procedure, new in SAS/QC^® 14.1, and illustrates how you can use rare events charts to improve health care quality.

Distinguished Research Statistician Developer Rick Wicklin gives you the inside scoop on Stephen Curry’s basketball shooting, including the fact that his probability of making a shot from the left side barely depends on the distance from the hoop. Wicklin also describes how to share SAS/IML® programs by creating packages with the PACKAGE statement and shows how you can visualize missing data with SAS. 

The following Computer Technology Workshops are being taught at JSM 2016 in Chicago this summer:

• Advanced ODS Graphics Examples in SAS
• Small Area Estimation Using SAS Software
• Weighted GEE Analysis Using SAS/STAT Software
• Current Methods in Survival Analysis Using SAS/STAT Software
  

The shortest path problem is to find the path between nodes in a graph such that the sum of the weights (such as costs) is minimized. Examples of shortest path problems include driving directions, network routing, operating schedules, and social networking. PROC OPTMODEL can be used to find the shortest path in a graph. Beginning in SAS 9.3 TS1M2, PROC OPTNET can also be used for this purpose.