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Applied longitudinal data analysis for epidemiology
Title statement Applied longitudinal data analysis for epidemiology : a practical guide / Jos W.R. Twisk, Department of Epidemiology and Biostatistics, Medical Center and the Department of Health Sciences of the Vrije Universteit, Amsterdam. Edition statement Second edition. Publication Cambridge : Cambridge University Press, 2013. Phys.des. 1 online resource (xiv, 321 pages) : digital, PDF file(s). ISBN 9781139342834 (ebook) Note Title from publisher's bibliographic system (viewed on 05 Oct 2015). Content note Machine generated contents note: 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. Notes to Availability Přístup pouze pro oprávněné uživatele Subj. Headings Epidemiology - Research - Statistical methods. * Epidemiology - Longitudinal studies. * Epidemiology - Statistical methods. Form, Genre elektronické knihy electronic books Country Anglie Language angličtina Document kind Electronic books URL Plný text pro studenty a zaměstnance UPOL 
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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.
Machine generated contents note: 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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