- English
- PDF
- Available on iOS & Android
About this book
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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Information
Table of contents
- Cover
- Half-title
- Series information
- Title page
- Copyright information
- Dedication
- Contents
- Preface
- 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 Different Outcome Variables
- 6 Explaining Differences between Groups
- 7 Multivariable Modelling
- 8 Predictions Based on Mixed Model Analysis
- 9 Mixed Model Analysis in Longitudinal Studies
- 10 Multivariate Mixed Model Analysis
- 11 Meta-Analysis on Individual Participant Data
- 12 Sample-Size Calculations
- 13 Some Loose Ends . . .
- References
- Index