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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.Read more
- Clearly understandable by non-statisticians
- Compares and contrasts different techniques and methods of analysis
- Illustrated with examples of real-life research questions
Reviews & endorsements
"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 ResearchSee more reviews
"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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- Date Published: April 2003
- format: Paperback
- isbn: 9780521525800
- length: 320 pages
- dimensions: 247 x 175 x 17 mm
- weight: 0.67kg
- contains: 54 b/w illus. 159 tables
- availability: Replaced by 9781107699922
Table of Contents
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
12. Software for longitudinal data-analysis
13. Sample size calculations
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