Analysis of Longitudinal and Incomplete Data


Prof. Geert Verbeke (Katholieke Universiteit Leuven and Universiteit Hasselt, Belgium) and Prof. Geert Molenberghs (Universiteit Hasselt and Katholieke Universiteit Leuven, Belgium).

Award-winning course

Prof. Molenberghs and Prof. Verbeke have received four Excellence in Continuing Education Awards from the American Statistical Association for this and related live courses.

Full-day course

Total duration: 6 hours 24 minutes.

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$999.00 (U.S. dollars)
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This Instant Short Course first presents 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 interpretational difficulties are bound to occur. Methods to properly analyze incomplete data, under flexible assumptions, are presented. Key concepts of sensitivity analysis are introduced.

Throughout the Instant Short 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.


Topics covered in this Instant Short Course:

  • Linear mixed models: General linear mixed model, Estimation and inference in the marginal model, Inference for the random effects. Duration: 1 hour 13 minutes.
  • Generalized estimating equations: Generalized linear models, Parametric modeling families, Generalized estimating equations (GEE), A family of GEE methods. Duration: 2 hour 24 minutes.
  • Generalized linear mixed models: Generalized linear mixed models, Marginal versus random-effects models. Duration: 1 hour 7 minutes.
  • Incomplete data: Proper analysis of incomplete data, Weighted generalized estimating equations, Multiple imputation, Sensitivity analysis for incomplete data. Duration: 1 hour 40 minutes.

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Live course

This Instant Short Course is based on a full-day live course which has been offered for over five years at the Joint Statistical Meetings.


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