JSM 2011 Online Program

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Activity Details


CE_01C Sat, 7/30/2011, 8:30 AM - 5:00 PM HQ-Americana Salon 1
Foundations and Recent Advances in Longitudinal and Incomplete Data- TWO DAY COURSE — Continuing Education Course
ASA
Instructor(s): Geert Verbeke, I-BioStat, Hasselt University, Geert Molenberghs, Universiteit Hasselt/Katholieke Universiteit Leuven
We first present 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. Apart from classical model building strategies, many of which have been implemented in standard statistical software, a number of flexible extensions and additional tools for model diagnosis will be indicated. 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, 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. Fourth, a selection of contemporary an highly relevant advances will be discussed:  The joint modeling of longitudinal and time-to-event outcomes;  Flexible modeling strategies for models with non-normally distributed random effects;  Recent advances in model diagnostics;  Modeling, fitting, and inferential strategies for (high-dimensional) multivariate longitudinal data;  The use of longitudinal data for discrimination and classification;  Robust and doubly robust estimation for incomplete data based on semi-parametric modeling (generalized estimating equations and pseudo-likelihood);  Strategies to undertake sensitivity analysis when data are incomplete, with an eye on both theoretical development as well as the regulatory framework for clinical trials and related studies. Throughout the course, it is assumed that the participants are familiar with basic statistical modeling, 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.



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