Jos W. R. Twisk

Applied Mixed Model Analysis (eBook, PDF)

A Practical Guide

• Format: PDF

Produktbeschreibung

This practical book is designed for applied researchers who want to use mixed models with their data. It discusses the basic principles of mixed model analysis, including two-level and three-level structures, and covers continuous outcome variables, dichotomous outcome variables, and categorical and survival outcome variables. Emphasizing interpretation of results, the book develops the most important applications of mixed models, such as the study of group differences, longitudinal data analysis, multivariate mixed model analysis, IPD meta-analysis, and mixed model predictions. All examples are analyzed with STATA, and an extensive overview and comparison of alternative software packages is provided. All datasets used in the book are available for download, so readers can re-analyze the examples to gain a strong understanding of the methods. Although most examples are taken from epidemiological and clinical studies, this book is also highly recommended for researchers working in other fields.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 18. April 2019
• Englisch
• ISBN-13: 9781108575393
• Artikelnr.: 56246400

Autorenporträt

Jos W. R. Twisk specializes in the methodological field of longitudinal data analysis and multilevel/mixed model analysis, about which he has written three textbooks. He has also authored a textbook on applied biostatistics in Dutch. He is director of the epidemiology Master's program of the Vrije Universiteit Medical Center, Amsterdam and Head of the Expertise Center for Applied Longitudinal Data Analysis. His main activities include applied methodological research, consulting, and teaching courses on mixed model analysis, longitudinal data analysis, multilevel analysis and applied basic statistics. He has authored and coauthored more than 650 peer-reviewed international papers.

Inhaltsangabe

1. Introduction
2. Basic principles of mixed model analysis
3. What is gained by using mixed model analysis?
4. Logistic mixed model analysis
5. Mixed model analysis with other outcomes
6. Explaining differences between groups
7. Multivariable modelling
8. Predictions based on mixed model analysis
9. Mixed model analysis for longitudinal data
10. Multivariate mixed model analysis
11. Sample size calculations
12. Some loose ends.