Discusses methods available for longitudinal data analysis in non-technical language, allowing readers to apply techniques easily to their work. Aimed at non-statisticians and researchers working in medical science and utilising longitudinal studies, the interpretation of the results of various methods of analysis is emphasised.
Discusses methods available for longitudinal data analysis in non-technical language, allowing readers to apply techniques easily to their work. Aimed at non-statisticians and researchers working in medical science and utilising longitudinal studies, the interpretation of the results of various methods of analysis is emphasised.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Jos W. R. Twisk is a Professor in the Department of Epidemiology and Data Science at Amsterdam Umc, Amsterdam, The Netherlands. He specialises in the methodological field of longitudinal data analysis and multilevel/mixed model analysis, and is head of the expertise center for Applied Longitudinal Data Analysis at the Amsterdam Umc.
Inhaltsangabe
1. Introduction 2. Continuous outcome variables 3. Continuous outcome variables - regression based methods 4. The modelling of time 5. Models to disentangle the between- and within-subjects relationship 6. Causality in observational longitudinal studies 7. Dichotomous outcome variables 8. Categorical and count outcome variables 9. Outcome variables with floor or ceiling effects 10. Analysis of longitudinal intervention studies 11. Missing data in longitudinal studies 12. Sample size calculations 13. Software for longitudinal data analysis.
1. Introduction 2. Continuous outcome variables 3. Continuous outcome variables - regression based methods 4. The modelling of time 5. Models to disentangle the between- and within-subjects relationship 6. Causality in observational longitudinal studies 7. Dichotomous outcome variables 8. Categorical and count outcome variables 9. Outcome variables with floor or ceiling effects 10. Analysis of longitudinal intervention studies 11. Missing data in longitudinal studies 12. Sample size calculations 13. Software for longitudinal data analysis.
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