In many studies, a continuous response variable is repeatedly measured over time on one or more subjects. The subjects might be grouped into different categories, such as cases and controls. The study of resulting observation profiles as functions of time is called functional data analysis. This paper shows how you can use the SSM procedure in SAS/ETS to model these functional data by using structural state space models (SSMs). A structural SSM decomposes a subject profile into latent components such as the group mean curve, the subject-specific deviation curve, and the covariate effects. The SSM procedure enables you to fit a rich class of structural SSMs, which permit latent components that have a wide variety of patterns. For example, the latent components can be different types of smoothing splines, including polynomial smoothing splines of any order and all L-splines up to order 2. The SSM procedure efficiently computes the restricted maximum likelihood (REML) estimates of the model parameters and the best linear unbiased predictors (BLUPs) of the latent components (and their derivatives). The paper presents several real-life examples that show how you can fit, diagnose, and select structural SSMs; test hypotheses about the latent components in the model; and interpolate and extrapolate these latent components.

Many retail and consumer packaged goods (CPG) companies are now keeping track of what their customers purchased in the past, often through some form of loyalty program. This record keeping is one example of how modern corporations are building data sets that have a panel structure, a data structure that is also pervasive in insurance and finance organizations. Panel data (sometimes called longitudinal data) can be thought of as the joining of cross-sectional and time series data. Panel data enable analysts to control for factors that cannot be considered by simple cross-sectional regression models that ignore the time dimension. These factors, which are unobserved by the modeler, might bias regression coefficients if they are ignored. This paper compares several methods of working with panel data in the PANEL procedure and discusses how you might benefit from using multiple observations for each customer. Sample code is available.

Researchers often use longitudinal data analysis to study the development of behaviors or traits. For example, they might study how an elderly person's cognitive functioning changes over time or how a therapeutic intervention affects a certain behavior over a period of time. This paper introduces the structural equation modeling (SEM) approach to analyzing longitudinal data. It describes various types of latent curve models and demonstrates how you can use the CALIS procedure in SAS/STAT to fit these models. Specifically, the paper covers basic latent curve models, such as unconditional and conditional models, as well as more complex models that involve multivariate responses and latent factors. All illustrations use real data that were collected in a study that looked at maternal stress and the relationship between mothers and their preterm infants. This paper emphasizes the practical aspects of longitudinal data analysis. In addition to illustrating the program code, it shows how you can interpret the estimation results and revise the model appropriately. The final section of the paper discusses the advantages and disadvantages of the SEM approach to longitudinal data analysis.

The success of an experimental study almost always hinges on how you design it. Does it provide estimates for everything you're interested in? Does it take all the experimental constraints into account? Does it make efficient use of limited resources? The OPTEX procedure in SAS/QC^® software enables you to focus on specifying your interests and constraints, and it takes responsibility for handling them efficiently. With PROC OPTEX, you skip the step of rifling through tables of standard designs to try to find the one that's right for you. You concentrate on the science and the analytics and let SAS do the computing. This paper reviews the features of PROC OPTEX and shows them in action, using examples from field trials and food science experimentation. PROC OPTEX is a useful tool for all these situations, doing the designing and freeing the scientist to think about the food and the biology. This paper, by longtime SAS user Cliff Pereira and Randy Tobias of SAS, tells you what you need to know.

A Chinese wind energy company designs several hundred wind farms each year. An important step in its design process is micrositing, in which it creates a layout of turbines for a wind farm. The amount of energy that a wind farm generates is affected by geographical factors (such as elevation of the farm), wind speed, and wind direction. The types of turbines and their positions relative to each other also play a critical role in energy production. Currently the company is using an open-source software package to help with its micrositing. As the size of wind farms increases and the pace of their construction speeds up, the open-source software is no longer able to support the design requirements. The company wants to work with a commercial software vendor that can help resolve scalability and performance issues. This paper describes the use of the OPTMODEL and OPTLSO procedures on the SAS^® High-Performance Analytics infrastructure together with the FCMP procedure to model and solve this highly nonlinear optimization problem. Experimental results show that the proposed solution can meet the company's requirements for scalability and performance.

Fitting mixed models to complicated data, such as data that include multiple sources of variation, can be a daunting task. SAS/STAT offers several procedures and approaches for fitting mixed models. This paper provides guidance on how to overcome obstacles that commonly occur when you fit mixed models by using the MIXED and GLIMMIX procedures. Examples are used to showcase procedure options and programming techniques that can help you overcome difficult data and modeling situations.

Waterfall plots have numerous applications. They are sometimes used in clinical trial analyses to indicate how patients respond to treatment. In an oncology trial, they might illustrate how tumor sizes change over the course of treatment. Rick Wicklin describes how to create waterfall plots by using the SGPLOT procedure. Another recent post discusses how to compute the rank of a matrix with SAS/IML^® software.

Introducing the SAS BCHOICE Procedure for Bayesian Choice Models (Computer Technology Workshop)

Analyzing Item Responses with the IRT Procedure: An Introduction with Applications (CTW)

Practical Finite Mixture Model with SAS (CTW)

Practical Bayesian Computation (full day)

Quantile Regression in Practice (half day)

Introduction to Structural Equation Modeling and Its Applications (half day)

This macro computes prediction intervals for generalized linear models that are fit using PROC GENMOD, similar to the prediction intervals available for normal-response models in the REG, GLM, and NLIN procedures but not available for generalized linear models. It also provides confidence intervals for the mean response in generalized linear models, but these intervals are symmetric around the predicted mean, unlike the confidence intervals available in PROC GENMOD, which are derived from confidence intervals around the linear predictor.