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Applied Longitudinal Data Analysis for Epidemiology (Softcover)
Twisk, J.
2ª Edición Junio 2013
Inglés
Tapa blanda
333 pags
618 gr
17 x 25 x 2 cm
ISBN 9781107699922
Editorial CAMBRIDGE
LIBRO IMPRESO
-5%
74,45 €70,73 €IVA incluido
71,59 €68,01 €IVA no incluido
Recíbelo en un plazo de
2 - 3 semanas
Description
This book discusses the most important techniques available for longitudinal data analysis, from simple techniques such as the paired t-test and summary statistics, to more sophisticated ones such as generalized estimating of equations and mixed model analysis. A distinction is made between longitudinal analysis with continuous, dichotomous and categorical outcome variables. The emphasis of the discussion lies in the interpretation and comparison of the results of the different techniques. The second edition includes new chapters on the role of the time variable and presents new features of longitudinal data analysis. Explanations have been clarified where necessary and several chapters have been completely rewritten. The analysis of data from experimental studies and the problem of missing data in longitudinal studies are discussed. Finally, an extensive overview and comparison of different software packages is provided. This practical guide is essential for non-statisticians and researchers working with longitudinal data from epidemiological and clinical studies.
Features
• New chapters discuss the role of the time variable and new features
of longitudinal data analysis
• An extensive overview and comparison of different software packages
helps users to determine which is most appropriate for their needs
• The text is supplemented with figures and examples of computer software
outputs to aid reader understanding of the techniques presented
Table of Contents
Preface
Acknowledgements
1. Introduction
2. Study design
3. Continuous outcome variables
4. Continuous outcome variables – relationships with other variables
5. The modelling of time
6. Other possibilities for modelling longitudinal data
7. Dichotomous outcome variables
8. Categorical and 'count' outcome variables
9. Analysis data from experimental studies
10. Missing data in longitudinal studies
11. Sample size calculations
12. Software for longitudinal data analysis
13. One step further
References
Index.
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