Models for Longitudinal and Incomplete Data

Second, models for non-Gaussian data will be discussed, with a strong emphasis on generalized estimating equations (GEE) and the generalized linear mixed model (GLMM). To usefully introduce this theme, a brief review of the classical generalized linear modeling framework will be presented.
Similarities and differences with the continuous case will be discussed. The differences between marginal models, such as GEE, and random-effects models, such as the GLMM, will be explained in detail.

Third, it is often necessary to consider fully non-linear models for longitudinal data. We will discuss such situations, and place some emphasis on the non-linear mixed-effects model.

Fourth, non-linear mixed models will be discussed. Applications in the PK/PD world will be brought to the front.

Fifth, when analyzing hierarchical and longitudinal data, one is often confronted with missing observations, i.e., scheduled measurements have not been made, due to a variety of (known or unknown) reasons. It will be shown that, if no appropriate measures are taken, missing data can cause seriously jeopardize results, and interpretation difficulties are bound to occur.
Methods to properly analyze incomplete data, under flexible assumptions, are presented. Key concepts of sensitivity analysis are introduced.

All developments will be illustrated with worked examples using the SAS System. However, the course is conceived such that it will be of benefit to both SAS users and users of other platforms.

Throughout the course, it will be assumed that the participants are familiar with basic statistical modeling concepts, including linear models (regression and analysis of variance), as well as generalized linear models (logistic and Poisson regression) and basic knowledge of mixed and multilevel models. Moreover, pre-requisite knowledge should also include general estimation and testing theory (maximum likelihood, likelihood ratio). When registering for this course, you have to mention the topics you have followed before and/or indicate where you became acquainted with the requested material.

• Verbeke, G. and Molenberghs, G. (2000) Linear Mixed Models for Longitudinal Data. New York: Springer.
• Molenberghs, G. and Kenward, M.G. (2007) Missing Data in Clinical Studies. Chichester: John Wiley & Sons.
• Molenberghs, G. and Verbeke, G. (2005) Models for Repeated Discrete Data. New York: Springer.

Geert Molenberghs and Geert Verbeke are editors and authors of several books on the use of linear mixed models for the analysis of longitudinal data (Springer Lecture Notes 1997, Springer Series in Statistics 2000, Springer Series in Statistics 2005, Chapman Hall/CRC 2007), and they have taught numerous short and longer courses on the topic in universities as well as industry, in Europe, North America, Latin America, and Australia. Both instructors received several Excellence in Continuing Education Awards of the American Statistical Association, for courses at Joint Statistical Meetings.