Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide
Author:
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Jos W. R. Twisk |
| Publisher: |
Cambridge University Press |
| Copyright: |
2003 |
| ISBN-13: |
978-0-521-52580-0 |
| Pages: |
301; paperback |
| Price: |
$67.25 |
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Comment from the Stata technical group
In Applied Longitudinal Data Analysis for Epidemiology: A Practical
Guide, Jos W. R. Twisk provides an intuitive introduction to estimation
techniques that are widely applied to longitudinal data by epidemiologists.
Twisk covers ANOVA, MANOVA, generalized estimating equations estimators for
longitudinal data, and random coefficient estimators. Rather than
developing a rigorous introduction to each method, Twisk builds an intuitive
foundation for each of the estimation frameworks and then uses these
foundations to discuss how to choose an estimation framework and interpret
the estimates. Also, not only does Twisk discuss the available software for
these estimators, but he compares the different implementations available in
various packages, including Stata.
Table of contents
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
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 GEE analysis
4.5.4.3 Different correlation structures
4.5.4.4 Unequally spaced time intervals
4.6 Random coefficient analysis
4.6.1 Introduction
4.6.2 Random coefficient analysis in longitudinal studies
4.6.3 Example
4.6.3.1 Results of a random coefficient analysis
4.6.3.2 Unequally spaced time intervals
4.6.4 Comments
4.7 Comparison between GEE analysis and random coefficient analysis
4.7.1 Extensions of random coefficient analysis
4.7.2 Equal variances over time
4.7.2.1 A numerical example
4.7.3 The correction for covariance
4.7.4 Comments
4.8 The modelling of time
4.8.1 Example
5 Other Possibilities for Modelling Longitudinal Data
5.1 Introduction
5.2 Alternative models
5.2.1 Time-lag model
5.2.2 Modelling of changes
5.2.3 Autoregressive model
5.2.4 Overview
5.2.5 Example
5.2.5.1 Introduction
5.2.5.2 Data structure for alternative models
5.2.5.3 GEE analysis
5.2.5.4 Random coefficient analysis
5.3 Comments
5.4 Another example
6 Dichotomous Outcome Variables
6.1 Simple methods
6.1.1 Two measurements
6.1.2 More than two measurements
6.1.3 Comparing groups
6.1.4 Example
6.1.4.1 Introduction
6.1.4.2 Development over time
6.1.4.3 Comparing groups
6.2 Relationships with other variables
6.2.1 ‘Traditional’ methods
6.2.2 Example
6.2.3 Sophisticated methods
6.2.4 Example
6.2.4.1 Generalized estimating equations
6.2.4.2 Random coefficient analysis
6.2.5 Comparison between GEE analysis and random coefficient analysis
6.2.6 Alternative models
6.2.7 Comments
7 Categorical and 'Count' Outcome Variables
7.1 Categorical outcome variables
7.1.1 Two measurements
7.1.2 More than two measurements
7.1.3 Comparing Groups
7.1.4 Example
7.1.5 Relationships with other variables
7.1.5.1 ‘Traditional’ methods
7.1.5.2 Example
7.1.5.3 Sophisticated methods
7.1.5.4 Example
7.2 'Count' outcome variables
7.2.1 Example
7.2.1.1 Introduction
7.2.1.2 GEE analysis
7.2.1.3 Random coefficient analysis
7.2.2 Comparison between GEE analysis and random coefficient analysis
8 Longitudinal Studies with Two Measurements: the definition and analysis of change
8.1 Introduction
8.2 Continuous outcome variables
8.2.1 A numerical example
8.2.2 Example
8.3 Dichotomous and categorical outcome examples
8.3.1 Example
8.4 Comments
8.5 Sophisticated analysis
8.6 Conclusions
9 Analysis of Experimental Studies
9.1 Introduction
9.2 Example with a continuous outcome variable
9.2.1 Introduction
9.2.2 Simple analysis
9.2.3 Summary statistics
9.2.4 MANOVA for repeated measurements
9.2.4.1 MANOVA for repeated measurements corrected for the baseline value
9.2.5 Sophisticated analysis
9.3 Example with a dichotomous outcome variable
9.3.1 Introduction
9.3.2 Simple analysis
9.3.3 Sophisticated analysis
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 Comments
10.6 Imputation methods
10.6.1 Continuous outcome variables
10.6.1.1 Cross-sectional imputation methods
10.6.1.2 Longitudinal imputation methods
10.6.1.3 Multiple imputation method
10.6.2 Dichotomous and categorical outcome variables
10.6.3 Example
10.6.3.1 Continuous outcome variables
10.6.3.2 Dichotomous outcome variables
10.6.4 Comments
10.7 Alternative approaches
10.8 Conclusions
11 Tracking
11.1 Introduction
11.2 Continuous outcome variables
11.3 Dichotomous and categorical outcome variables
11.4 Example
11.4.1 Two measurements
11.4.2 More than two measurements
11.5 Comments
11.5.1 Interpretation of tracking coefficients
11.5.2 Risk factors for chronic diseases
11.5.3 Grouping of continuous outcome variables
11.6 Conclusions
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 S-PLUS
12.2.4 Overview
12.3 GEE analysis with dichotomous outcome variables
12.3.1 STATA
12.3.2 SAS
12.3.3 S-PLUS
12.3.4 Overview
12.4 Random coefficient analysis with continuous outcome variables
12.4.1 STATA
12.4.2 SAS
12.4.3 S-PLUS
12.4.4 SPSS
12.4.5 MLwiN
12.4.6 Overview
12.5 Random coefficient analysis with dichotomous outcome variables
12.5.1 Introduction
12.5.2 STATA
12.5.3 SAS
12.5.4 MLwiN
12.5.5 Overview
12.6 Categorical and ‘count’ outcome variables
12.7 Alternative approach using covariance structures
12.7.1 Example
13 Sample Size Calculations
13.1 Introduction
13.2 Example
References
Index
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