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A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
Activity Details
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| CE_01C | Sun, 8/4/2013, 8:30 AM - 5:00 PM | W-Fortifications | |
| Foundations and Recent Advances in Longitudinal and Incomplete Data and in Joint Modeling — Continuing Education Course | |||
| ASA | |||
| Instructor(s): Geert Molenberghs, Universiteit Hasselt & Katholieke Universiteit Leuven, Dimitris Rizopoulos, Erasmus MC | |||
| We first present methods for hierarchical data. We set out with the linear mixed models for continuous hierarchical data. The focus lies on the modeler's perspective and on applications. Emphasis will be on model formulation, parameter estimation, and hypothesis testing, as well as on the distinction between the random-effects (hierarchical) model and the implied marginal model. Also, models for non-Gaussian data will be discussed, with emphasis on generalized estimating equations (GEE) and the generalized linear mixed model (GLMM). 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. Second, 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. Third, repeated 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 b etter insight into the mechanisms that underlie the phenomenon under study. We 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. Throughout the course, it is assumed that the participants are familiar with basic statistical modelling, including linear models (regression and analysis of variance), as well as generalized linear models (logistic and Poisson regression). Moreover, pre-requisite knowledge should also include general estimation and testing theory (maximum likelihood, likelihood ratio). All developments will be illustrated with worked examples using the SAS System. Prerequisites. 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) Verbeke and Molenberghs; The Statistical Analysis of Failure Time Data, 2nd Edition, Chapters 1-4 (Wiley) Kalbfleisch and Prentice. | |||
2013 JSM Online Program Home
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