PERIODO E HORÁRIO: 12 a 16 de novembro de 2018, das 09 ás 12h e das 14 ás 17h.
LOCAL: Departamento de Ciências Exatas, Prédio da Engenharia, ESALQ/USP, Sala 315, na Avenida Pádua Dias, 11, em Piracicaba, SP
OBJETIVO: Esse workshop discutirá temas atuais e muito importantes dentro da Estatística, envolvendo modelos lineares mistos, e trará grandes contribuições para nossos alunos e profissionais em geral da área de Estatística.
Abstract: 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 analysing 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 workshop, it will be 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. These will be supplemented with practical sessions.
Presenters: Geert Molenberghs is Professor of Biostatistics at the Universiteit Hasselt and the Katholieke Universiteit Leuven in Belgium. He received the B.S. degree in mathematics (1988) and a Ph.D. in biostatistics (1993) from the Universiteit Antwerpen. Dr Molenberghs published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and on the analysis of non-response in clinical and epidemiological studies. He served as Joint Editor for Applied Statistics (2001-2004) and is President of the International Biometric Society (2004-2005). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society. He has held visiting positions at the Harvard School of Public Health (Boston, MA). He is currently Co-Editor of Biometrics (2007–2009).
Both presenters are editor and author of four 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 2009), 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.