This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
|CE_18C||Tue, 8/3/2010, 8:30 AM - 5:00 PM||CC-2&3 (East)|
|Joint Modeling Approaches in Longitudinal Studies Using Random Effects — Continuing Education Course|
|Instructor(s): Geert Molenberghs, I-BioStat, Geert Verbeke, I-Biostat, Dimitris Rizopoulos, Erasmus University Medical Center|
|In longitudinal studies, measurements are often collected for different types of outcomes for each subject. These may include several longitudinally measured responses (such as blood values relevant to the medical condition under study) and the time at which an event of particular interest occurs (e.g., death, development of a disease or dropout from the study). These outcomes are often separately analyzed; however, in many instances, a joint modeling approach is either required or may produce better insight into the mechanisms that underlie the phenomenon under study. The aim of this course is to first identify the type of research questions that require joint modeling, and then present state of the art statistical models that are designed to optimally use the data to answer those questions. Emphasis is placed on three settings: (1) longitudinal studies with nonrandom dropout; (2) time-to-event analysis with time-dependent covariates measured with error; (3) multivariate longitudinal data scenarios where the aim is to study the association structure. These joint modeling approaches are presented within a unified framework that is based on the use of random effects to explain the interdependencies between the observed outcomes. Course attendees should consider as a prerequisite for the course familiarity with the subject at the level of: Linear Mixed Models for Longitudinal Data, Chapters 1-7 (Springer-Verlag) Verbeke and Molenberghs; The Statistical Analysis of Failure Time Data, 2nd Edition, Chapters 1-4 (Wiley) Kalbfleisch and Prentice.|