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More About This Textbook
Overview
The most important techniques available for longitudinal data analysis are discussed in this book. The discussion includes simple techniques such as the paired t-test and summary statistics, but also more sophisticated techniques such as generalized estimating equations and random coefficient analysis. A distinction is made between longitudinal analysis with continuous, dichotomous, and categorical outcome variables. This practical guide is especially suitable for non-statisticians and all those undertaking medical research or epidemiological studies.
Editorial Reviews
From the Publisher
"The book will be a welcome addition and is generally well written."Max K Bulsara, School of Population Health, University of Western Australia, Statistical Methods in Medical Research
"I highly recommend Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide. I believe that it will be useful for applied statisticians, public health researchers, clinical investigators, and epidemiologists. One can use this book for learning more about longitudinal data analysis (particularly regarding modern statistical techniques) and for teaching and consulting purposes."
Richard A. Oster for Teaching of Statistics in the Health Sciences
"...understandable, stimulating, and practical."
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Meet the Author
Dr Jos W. R. Twisk is Senior Researcher and Lecturer in the Department of Clinical Epidemiology and Biostatistics and the Institute for Research in Extramural Studies, Vrije Universitiet, Medical Centre, Amsterdam.
Table of Contents
1. Introduction; 2. Study design; 3. Continuous outcome variables; 4. Continuous outcome variables - relationships with other variables; 5. Other possibilities to model longitudinal data; 6. Dichotomous outcome variables; 7. Categorical and 'count' outcome variables; 8. Longitudinal studies with two measurements: the definition and analysis of change; 9. Analysis of experimental studies; 10. Missing data in longitudinal studies; 11. Tracking; 12. Software for longitudinal data-analysis; 13. Sample size calculations; Index.