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E-raamat: Applied Mixed Model Analysis: A Practical Guide

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(Universiteit van Amsterdam)
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Applied Mixed Model Analysis: A Practical GuideSuurem pilt
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This book explains all aspects of mixed model analysis without mathematical jargon, so that non-statisticians can understand the basic principles, analyze their own data, and interpret the results with confidence. Worked examples are analyzed with STATA, and all datasets are available for download, equipping readers to replicate the methods.

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.

Muu info

Emphasizing interpretation of results, this hands-on guide explains why, when, and how to use mixed models with your data.
Preface xi
1 Introduction 1(4)
1.1 Introduction
1(1)
1.2 Background of Mixed Model Analysis
1(2)
1.3 General Approach
3(1)
1.4 Prior Knowledge
4(1)
1.5 Example Datasets
4(1)
1.6 Software
4(1)
2 Basic Principles of Mixed Model Analysis 5(31)
2.1 Introduction
5(4)
2.2 Example
9(4)
2.3 Intraclass Correlation Coefficient
13(3)
2.4 Random Slopes
16(2)
2.5 Example
18(6)
2.6 Mixed Model Analysis with More than Two Levels
24(7)
2.7 Assumptions in Mixed Model Analysis
31(1)
2.8 Comments
32(4)
3 What Is Gained by Using Mixed Model Analysis? 36(9)
3.1 Introduction
36(1)
3.2 Example with a Balanced Dataset
36(5)
3.3 Example with an Unbalanced Dataset
41(2)
3.4 Cluster Randomisation
43(1)
3.5 Comments
43(2)
4 Logistic Mixed Model Analysis 45(13)
4.1 Introduction
45(1)
4.2 Example
45(6)
4.3 Intraclass Correlation Coefficient in Logistic Mixed Model Analysis
51(1)
4.4 Different Estimation Procedures
52(1)
4.5 Other Ways to Adjust for the Correlated Observations
52(6)
5 Mixed Model Analysis with Different Outcome Variables 58(20)
5.1 Introduction
58(1)
5.2 Categorical Outcome Variables
58(7)
5.3 Count Outcome Variables
65(6)
5.4 Survival Data
71(6)
5.5 Other Outcomes
77(1)
6 Explaining Differences between Groups 78(12)
6.1 Introduction
78(1)
6.2 Example
79(11)
7 Multivariable Modelling 90(26)
7.1 Introduction
90(9)
7.2 Prediction Models and Association Models
99(15)
7.3 Prediction and Validation
114(1)
7.4 Comments
115(1)
8 Predictions Based on Mixed Model Analysis 116(15)
8.1 Introduction
116(1)
8.2 Shrinkage
116(5)
8.3 Different Possibilities to Obtain Predicted Values
121(6)
8.4 Comments
127(4)
9 Mixed Model Analysis in Longitudinal Studies 131(20)
9.1 Introduction
131(1)
9.2 Longitudinal Studies
131(8)
9.3 Hybrid Models to Disentangle Between-Subject and Within-Subject Effects
139(2)
9.4 Growth Curve Analysis
141(6)
9.5 Other Methods to Analyse Longitudinal Data
147(2)
9.6 Comments
149(2)
10 Multivariate Mixed Model Analysis 151(15)
10.1 Introduction
151(2)
10.2 Example
153(10)
10.3 Comments
163(3)
11 Meta-Analysis on Individual Participant Data 166(13)
11.1 Introduction
166(1)
11.2 Example
167(8)
11.3 Comments
175(4)
12 Sample-Size Calculations 179(8)
12.1 Introduction
179(1)
12.2 Standard Sample-Size Calculations
180(1)
12.3 Sample-Size Calculations for Mixed Model Studies
181(1)
12.4 Example
181(1)
12.5 Which Sample-Size Calculation Should Be Used?
182(3)
12.6 Comments
185(2)
13 Some Loose Ends... 187(40)
13.1 The xt Procedures in STATA
187(4)
13.2 Hybrid Models Revisited
191(4)
13.3 Bayesian Mixed Model Analysis
195(9)
13.4 Software
204(23)
References 227(7)
Index 234
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.
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