Mixed Effects Models with Applications

Dr. Andrzej Galecki, Mr. Brady West

Aim of Course:

This course will explain the basic theory of linear and non-linear mixed effects models, including hierarchical linear models (HLM). It will outline the algorithms used for estimation, primarily for models involving normally distributed errors, and will provide examples of data analysis. The course aims at providing a basic understanding and knowledge of the mixed effect models that will allow you to use them in practice. To achieve this aim an implementation of the mixed effects models in R and SAS to real data will also be presented and discussed.

Mixed models are a powerful class of models used for the analysis of correlated data. Examples of correlated data include, but are not limited to, clustered data, repeated observations, longitudinal data, multiple dependent variables, spatial data or data from population pharmacokinetic/pharmacodynamic studies. A key feature of mixed models is that, by introducing random effects in addition to fixed effects, they allow you to address multiple source of variation, e.g. in the longitudinal study they allow you to take into account both within- and between- subject variation.

The course will detail a basic theory of mixed models and will provide an overview of estimation methods, with emphasis on the linear case. Concepts of mixed models will be illustrated with examples analyzed using PROC/MIXED in SAS and functions in R. Most of the illustrations will have parallel examples in Stata and/or SPSS (not all features are available in those packages).

Who Should Take This Course:

Researchers who analyze experiments, clinical trials or surveys where the data is not amenable to very simple statistical analysis.

Course Program:

The course is structured as follows

The Instructor:

Andrzej Galecki is a Research Professor in the Geriatrics Center/Institute of Gerontology at the Medical School and has a joint appointment as Associate Research Scientist in the Department of Biostatistics at the School of Public Health of the University of Michigan in Ann Arbor. His research interests lie in development and application of statistical methods for analyzing correlated and overdispersed data. He is co-author of the Linear Mixed Models: A Practical Guide using Statistical Software book by B. West et al.

Brady West is Lead Statistician at the Center for Statistical Consultation and Research (CSCAR) at the University of Michigan. He is the lead author of Linear Mixed Models: A Practical Guide using Statistical Software. He specializes in applications of statistical software and analysis of survey data, and his primary research interests revolve around regression models for clustered and longitudinal data.

Organization of the Course:

The course takes place over the internet, at statistics.com. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor. The course is scheduled to take place over 4 weeks, and typically requires 10-15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials and work through exercises. Discussion among participants is encouraged. The instructor will provide answers and comments.

Certificates and Grades:

You may be interested only in learning the material presented, and not be concerned with grades or certificates. Or you may be enrolled in a statistics.com Professional Advancement Program that requires demonstration of proficiency in the subject, in which case your work will be assessed for purposes of issuing a grade. Or you may require only a "Certificate of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). As you begin the class, you will be asked to specify your category.

Credit:

This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a certificate will be issued by statistics.com, upon request.

Dates:

May. 9 - Jun. 6, 2008
Click here to be notified of future course offerings.

Participants gain access to the online materials on the first day of the course, and typically spend about 10-15 hours per week (at their convenience). You retain full access to course materials, including discussion board, for two weeks after the course closing date.

Level:

Advanced-Intermediate

Prerequisite:

The equivalent of Introduction to Statistics I: Inference for a Single Variable, and Introduction to Statistics II: Working with Bivariate Data (and, if necessary before these courses, Introduction to Statistics for Beginners or Survey of Statistics for Beginners). In addition to basic probability and statistics, participants should be familiar with multiple linear regression, basic matrix algebra and analysis of variance, and should keep in mind that this course involves presentation of theory. For a more complete coverage of regression, and to gain greater comfort with the presentation of theory, see Regression Analysis and Logistic Regression.

Course Text:

The course text is Linear Mixed Models: A Practical Guide using Statistical Software by B. West, Welch, Gillespie and Galecki, CRC Press. This text can be ordered directly from CRC press using this form. CRC Press usually gives a 25% discount when the book is ordered using the above form.

Software:

Concepts of mixed models will be illustrated with examples analyzed using PROC/MIXED in SAS and functions in R. Most of the illustrations for the linear case will have parallel examples in Stata and/or SPSS (not all features are available in those packages). Exercises should be doable with SAS, R, Stata, or SPSS. For information on obtaining software, click here.

Registration:

Register Online - $449
Register Online (academic) - $349 (you must be affiliated with a college, university or high school)

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Note: Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.