Longitudinal Data Analysis Using SAS
A 2-Day Seminar Taught by Paul D. Allison, Ph.D.
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.
This seminar will use SAS for the many empirical examples and the exercises. However, lecture notes and exercises using Stata 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. Power outlets will be provided at each seat.
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, MATERIALS
The seminar meets Friday, December 8 and Saturday, December 9 at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103. The class will meet from 9 to 5 each day with a 1-hour lunch break.
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 includes all course materials. The early registration fee of $895.00 is available until November 8.
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 Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $134. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code STAT12 or click here. For guaranteed rate and availability, you must reserve your room no later than Tuesday, November 7.
If you make reservations after the cut-off date ask for the Statistical Horizon’s room rate (do not use the code) and they will try to accommodate your request.
- Opportunities and challenges of panel data.
a. Data requirements
b. Benefits of panel data
c. Problem of dependence
d. Software considerations
- Linear models
a. Robust standard errors
b. Generalized estimating equations
c. Random effects models
d. Fixed effects models
e. Between-within models
- 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
- Models for count data
a. Poisson vs. negative binomial models
b. GEE and random effects
c. Fixed effects and between-within models
- Linear structural equation models
a. Fixed and random effects in the SEM context
b. Models for reciprocal causation with lagged effects
“Dr. Paul Allison’s course on Longitudinal Data Analysis Using SAS was very informative and well constructed. In 2 days, we covered all the basics of LDA for continuous, categorical and count data. Paul is a great instructor and he patiently answered all of our questions. I would definitely recommend this course to all epidemiologists and biostatisticians!”
Vidhya Parameswaran, Fresenius Medical Care
“This course was very well organized with many practical examples that applied to our field. Dr. Allison explained the material very well and it was not difficult to understand. I would recommend this course to my friends and colleagues who are interested in learning longitudinal data analysis.”
Hasan Al-Sayegh, Boston Children’s Hospital
“This course was very helpful. Now, I am better able to understand the choices of techniques for analyzing my data and can effectively interpret, explain and defend the results. “
Susan Paulukonis, California Department of Public Health
“Dr. Allison is an amazing teacher. As a clinician, I was able to not only comprehend this relatively advanced statistical concept, but take what I learned in this course and apply it to ongoing research projects. Paul is able to explain complicated materials in a very clear manner. Highly recommend this course to anyone interested in longitudinal data analysis. The Statistical Horizons’ staff is also great.”
Michael Bowdish, University of South California, Keck School of Medicine
“This is a very practical course. I have been doing LDA for many years. For the first time, I have a clear idea of LDA across all methods and all types of data. If you have done LDA before, Dr. Allison will bring your skills and knowledge of LDA to the next level- higher and better!
Suhong Tong, University of Colorado Denver, School of Medicine- Pediatric
“The course, Longitudinal Data Analysis, provided very clear explanations of key concepts and details on a wide variety of longitudinal models. The materials were very comprehensive and intuitive.”
Margaret Sheridan, Sacramento Municipal Utility District
“I really enjoyed this course because of the following elements: organized structure and materials; the inclusion of exercises and specific examples for each topic; and Dr. Allison is always open to questions.”
Masayoshi Shibata, Santander