"In this book the most important methods available for longitudinal data analysis are discussed. This discussion includes simple methods such as the paired t-test and summary statistics, and also more sophisticated methods such as generalized estimating equations and mixed model analysis. A distinction is made between longitudinal data analysis with continuous, dichotomous, categorical and other outcome variables"--
"In this book the most important methods available for longitudinal data analysis are discussed. This discussion includes simple methods such as the paired t-test and summary statistics, and also more sophisticated methods such as generalized estimating equations and mixed model analysis. A distinction is made between longitudinal data analysis with continuous, dichotomous, categorical and other outcome variables"--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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