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Applied Mixed Model Analysis: A Practical Guide: Practical Guides to Biostatistics and Epidemiology

Autor Jos W. R. Twisk
en Limba Engleză Paperback – 17 apr 2019
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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Specificații

ISBN-13: 9781108727761
ISBN-10: 110872776X
Pagini: 246
Ilustrații: 34 b/w illus.
Dimensiuni: 173 x 246 x 14 mm
Greutate: 0.45 kg
Ediția:2Revizuită
Editura: Cambridge University Press
Colecția Cambridge University Press
Seria Practical Guides to Biostatistics and Epidemiology

Locul publicării:New York, United States

Cuprins

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.

Notă biografică


Descriere

Emphasizing interpretation of results, this hands-on guide explains why, when, and how to use mixed models with your data.