Longitudinal Data Analysis Using SAS

A 2-Day Seminar Taught by Paul D. Allison, Ph.D.

Read reviews of this seminar 

To see a sample of the course materials, click here.


PANEL DATA OFFER MAJOR OPPORTUNITIES AND SERIOUS PITFALLS

The most common type of longitudinal data is panel data, consisting of measurements of predictor and response variables at two or more points in time for many individuals. Such data have two major attractions: the ability to control for unobservables, and the determination of causal ordering.

However, there is also a major difficulty with panel data: repeated observations are typically correlated and this invalidates the usual assumption that observations are independent. As a result, confidence intervals and p-values can be severely biased. In some cases, coefficients may also be biased downward.

This course covers four methods for solving the problem of dependent observations: robust standard errors, generalized estimating equations, random effects models and fixed effects models. You’ll learn how to use these methods for quantitative outcomes, categorical outcomes, and count data outcomes. You’ll also learn which methods are best suited for different kinds of applications.

This is a hands-on seminar with ample opportunities to practice these new methods.

Here are a few of the topics you won’t want to miss:

  • How to use panel data to control for unobserved variables.
  • Why fixed effects methods often give very different results from random effects methods.
  • How to reshape data from long form to wide form and back again.
  • Why the default correlation structure for GEE is usually not the best.
  • The difference between maximum likelihood and restricted maximum likelihood.
  • How to estimate and interpret random coefficient models.
  • Why first-order autoregressive structures are usually unsatisfactory.
  • The difference between subject-specific coefficients and population-averaged coefficients, and why it matters.
  • How to do longitudinal analysis using ordered logit or multinomial logit.

In this seminar, we will use the following SAS procedures: GLM, SURVEYREG, GENMOD, MIXED, LOGISTIC, SURVEYLOGISTIC, GLIMMIX, and CALIS. Lecture notes using Stata are available on request from registered participants.


COMPUTING

This seminar will use SAS for the many empirical examples and the exercises. However, lecture notes and exercises using Stata and R are also available on request. At least one hour each day will be devoted to exercises. To optimally benefit, you should bring your own laptop with a recent version of SAS (or Stata) installed.

There is now a free version of SAS, called the SAS University Edition, that is available to anyone. It has everything needed to run the exercises in this course, and it will run on Windows, Mac or Linux computers. However, you do need a 64-bit machine with at least 1 GB of RAM. You also have to download and install virtualization software that is available free from third-party vendors. The SAS Studio interface runs in your browser, but you do not have to be connected to the Internet. The download and installation are a bit complicated, but well worth the time and effort.  


WHO SHOULD ATTEND? 

If you need to analyze longitudinal data and have a basic statistical background, this seminar is for you. You should have a good working knowledge of the principles and practice of multiple regression, as well as elementary statistical inference. It is also helpful to have some familiarity with logistic regression. But you do not need to know matrix algebra, calculus, or likelihood theory.  


LOCATION, Format, and MATERIALS

The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at the Courtyard Atlanta Downtown, 133 Carnegie Way, Atlanta, GA, 30303.

Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.


Registration and lodging

The fee of $995.00 includes all course materials. The early registration fee of $895.00 is available until April 30.

Refund Policy

If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50). 

Lodging Reservation Instructions

A block of guest rooms has been reserved at the Courtyard Atlanta Downtown, 133 Carnegie Way, Atlanta, GA, 30303, where the seminar takes place, at a special rate of $140. In order to make reservations, call 800-321-2211 and identify yourself as part of the Statistical Horizons room block, or click here. For guaranteed rate and availability, you must reserve your room no later than Wednesday, May 8, 2019 at 5:00pm ET.

We also recommend going directly to the hotel’s website or checking other online hotel sites. Pricing varies and you may be able to secure a better rate. 


SEMINAR OUTLINE

  1. Opportunities and challenges of panel data.
            a. Data requirements
            b. Benefits of panel data
            c. Problem of dependence
            d. Software considerations
  2. Linear models
            a. Robust standard errors
            b. Generalized least squares
            c. Random effects models
            d. Fixed effects models
            e. Between-within models
  3. Logistic regression models
           a. Robust standard errors
           b. Generalized estimating equations
           c. Subject-specific vs. population averaged methods
           d. Random effects models
           e. Fixed effects models
           f.  Between-within models
  4. Models for count data     
           a. Poisson vs. negative binomial models
           b. GEE and random effects
           c. Fixed effects and between-within models
  5. Linear structural equation models
           a. Fixed and random effects in the SEM context
           b. Models for reciprocal causation with lagged effects

COMMENTS BY RECENT PARTICIPANTS

“This course provided me with all the tools needed to perform longitudinal analyses (the appropriate type) on my data. The course materials provided by Dr. Allison are so clear and concise and provide a step by step instruction of how to write SAS code and how to interpret it. I will absolutely be taking more courses in the future with Statistical Horizons.”
  Erin Dursa, Department of Veterans Affairs

“Longitudinal Data Analysis Using SAS was a beneficial short course that clarified some of my questions and provided insights to additional details in the SAS procedures used in the course. I recommend this course to anyone who would like to learn about this topic for the first time, needs a refresher on details on applying these methods, or needs to widen their SAS modeling skills.”
  James Robinson, Conatus Pharmaceuticals

“This course strikes a strong balance between providing comprehensive theoretical information and the implementation of the data analytic methods. There was ample time to ask questions and apply the techniques yourself.”
  Skye Fitzpatrick, Rutgers University

“Dr. Allison is an excellent instructor and one of the best teachers of statistics I have ever encountered. He painstakingly explains each of the models for longitudinal data analysis, the assumptions underlying them, and the best situations to apply them. He is also careful to highlight their limitations and practical tradeoffs between different models.”
  Chukwuma Mbaeyi, Centers for Disease Control and Prevention

“The course packet is very helpful (and fortunately offered in a variety of statistical programs). I will certainly refer to it when analyzing longitudinal data in the future.”
  Jake Aronoff, Northwestern University

“My favorite aspect of the course was the hands-on nature. I feel like I can go back to the office and start using what I learned right away.”
  Reside Jacob, OCHIN