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Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide, Second Edition

Author:
Jos W. R. Twisk
Publisher: Cambridge University Press
Copyright: 2013
ISBN-13: 978-1-107-69992-2
Pages: 321; paperback
Price: $54.75

Comment from the Stata technical group

Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide, Second Edition, by Jos W. R. Twisk, provides a practical introduction to the estimation techniques used by epidemiologists for longitudinal data. Topics include ANOVA, MANOVA, generalized estimating equation estimators, and mixed models. Continuous, dichotomous, and count outcomes are considered. Rather than providing a rigorous theoretical discussion of each estimator, Twisk builds an intuitive foundation for each method and then provides examples and a discussion of how to interpret the estimates. Stata is used to produce virtually all the example output in the text. Twisk’s book is suitable for use as a supplementary text in an applied statistics course for epidemiologists and is also a succinct reference for practicing epidemiological researchers.


Table of contents

Table of Contents
Preface
Acknowledgements
1 Introduction
1.1 Introduction
1.2 General approach
1.3 Prior knowledge
1.4 Example
1.5 Software
1.6 Data structure
1.7 Statistical notation
1.8 What’s new in the second edition?
2 Study design
2.1 Introduction
2.2 Observational longitudinal studies
2.2.1 Period and cohort effects
2.2.2 Other confounding effects
2.2.3 Example
2.3 Experimental (longitudinal) studies
3 Continuous outcome variables
3.1 Two measurements
3.1.1 Example
3.2 Non-parametric equivalent of the paired t-test
3.2.1 Example
3.3 More than two measurements
3.3.1 The “univariate” approach: a numerical example
3.3.2 The shape of the relationship between an outcome variable and time
3.3.3 A numerical example
3.3.4 Example
3.4 The “univariate” or the “multivariate” approach?
3.5 Comparing groups
3.5.1 The “univariate” approach: a numerical example
3.5.2 Example
3.6 Comments
3.7 Post-hoc procedures
3.7.1 Example
3.8 Different contrasts
3.8.1 Example
3.9 Non-parametric equivalent of MANOVA for repeated measurements
3.9.1 Example
4 Continuous outcome variables — relationships with other variables
4.1 Introduction
4.2 “Traditional” methods
4.3 Example
4.4 Longitudinal methods
4.5 Generalized estimating equations
4.5.1 Introduction
4.5.2 Working correlation structures
4.5.3 Interpretation of the regression coefficients derived from GEE analysis
4.5.4 Example
4.5.4.1 Introduction
4.5.4.2 Results of a GEE analysis
4.5.4.3 Different correlation structures
4.6 Mixed model analysis
4.6.1 Introduction
4.6.2 Mixed models for longitudinal studies
4.6.3 Example
4.6.4 Comments
4.7 Comparison between GEE analysis and mixed model analysis
4.7.1 The “adjustment for covariance” approach
4.7.2 Extensions of mixed model analysis
4.7.3 Comments
5 The modeling of time
5.1 The development over time
5.2 Comparing groups
5.3 The adjustment for time
6 Other possibilities for modeling longitudinal data
6.1 Introduction
6.2 Alternative models
6.2.1 Time-lag model
6.2.2 Model of changes
6.2.3 Autoregressive model
6.2.4 Overview
6.2.5 Example
6.2.5.1 Introduction
6.2.5.2 Data structure for alternative models
6.2.5.3 GEE analysis
6.2.5.4 Mixed model analysis
6.3 Comments
6.4 Another example
7 Dichotomous outcome variables
7.1 Simple methods
7.1.1 Two measurements
7.1.2 More than two measurements
7.1.3 Comparing groups
7.1.4 Example
7.1.4.1 Introduction
7.1.4.2 Development over time
7.1.4.3 Comparing groups
7.2 Relationships with other variables
7.2.1 “Traditional” methods
7.2.2 Example
7.2.3 Sophisticated methods
7.2.4 Example
7.2.4.1 Generalized estimating equations
7.2.4.2 Mixed model analysis
7.2.5 Comparison between GEE analysis and mixed model analysis
7.2.6 Alternative models
7.2.7 Comments
8 Categorical and “count” outcome variables
8.1 Categorical outcome variables
8.1.1 Two measurements
8.1.2 More than two measurements
8.1.3 Comparing groups
8.1.4 Example
8.1.5 Relationship with other variables
8.1.5.1 “Traditional” methods
8.1.5.2 Example
8.1.5.3 Sophisticated methods
8.1.5.4 Example
8.2 “Count” outcome variables
8.2.1 Example
8.2.1.1 Introduction
8.2.1.2 GEE analysis
8.2.1.3 Mixed model analysis
8.2.2 Comparison between GEE analysis and mixed model analysis
8.3 Comments
9 Analysis of experimental studies
9.1 Introduction
9.2 Continuous outcome variables
9.2.1 Experimental models with only one follow-up measurement
9.2.1.1 Example
9.2.2 Experimental studies with more than one follow-up measurement
9.2.2.1 Simple analysis
9.2.2.2 Summary statistics
9.2.2.3 MANOVA for repeated measurements
9.2.2.4 MANOVA for repeated measurements adjusted for the baseline value
9.2.2.5 Sophisticated analysis
9.2.3 Conclusion
9.3 Dichotomous outcome variables
9.3.1 Introduction
9.3.2 Simple analysis
9.3.3 Sophisticated analysis
9.3.4 Other approaches
9.4 Comments
10 Missing data in longitudinal studies
10.1 Introduction
10.2 Ignorable or informative missing data?
10.3 Example
10.3.1 Generating datasets with missing data
10.3.2 Analysis of determinants for missing data
10.4 Analysis performed on datasets with missing data
10.4.1 Example
10.5 Imputation methods
10.5.1 Continuous outcome variables
10.5.1.1 Cross-sectional imputation methods
10.5.1.2 Longitudinal imputation methods
10.5.1.3 Comment
10.5.1.4 Multiple imputation
10.5.2 Dichotomous and categorical outcome variables
10.5.3 Example
10.5.3.1 Continuous outcome variables
10.5.3.2 Should multiple imputation be used in combination with a mixed model analysis?
10.5.3.3 Additional analyses
10.5.3.4 Dichotomous outcome variables
10.5.4 Comments
10.5.4.1 Alternative approaches
10.6 GEE analysis versus mixed model analysis regarding the analysis on datasets with missing data
10.7 Conclusions
11 Sample size calculations
11.1 Introduction
11.2 Example
12 Software for longitudinal data analysis
12.1 Introduction
12.2 GEE analysis with continuous outcome variables
12.2.1 Stata
12.2.2 SAS
12.2.3 R
12.2.4 SPSS
12.2.5 Overview
12.3 GEE analysis with dichotomous outcome variables
12.3.1 Stata
12.3.2 SAS
12.3.3 R
12.3.4 SPSS
12.3.5 Overview
12.4 Mixed model analysis with continuous outcome variables
12.4.1 Stata
12.4.2 SAS
12.4.3 R
12.4.4 SPSS
12.4.5 MLwiN
12.4.6 Overview
12.5 Mixed model analysis with dichotomous outcome variables
12.5.1 Introduction
12.5.2 Stata
12.5.3 SAS
12.5.4 R
12.5.5 SPSS
12.5.6 MLwiN
12.5.7 Overview
12.6 Categorical and “count” outcome variables
12.7 The “adjustment for covariance approach”
12.7.1 Example
13 One step further
13.1 Introduction
13.2 Outcome variables with upper or lower censoring
13.2.1 Introduction
13.2.2 Example
13.2.3 Remarks
13.3 Classification of subjects with different developmental trajectories
References
Index
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