1st Edition
Applied Linear Regression for Longitudinal Data With an Emphasis on Missing Observations
1. Scientific Framework of Data Analysis
2. Revisiting and Shortcomings of Standard Linear Regression Models
3. An Introduction to the Analysis of Longitudinal Data
4. Model Building for Longitudinal Data Analysis
5. Analysis of a Pre/Post Measurement Design
6. Analysis of Longitudinal Life-Event Studies
7. Analysis of Longitudinal Experimental Studies
Biography
Frans E.S. Tan is an associate professor (retired) of methodology and statistics at Maastricht University, The Netherlands.
Shahab Jolani is an assistant professor of methodology and statistics at Maastricht University, The Netherlands.
"Overall, the book is well written. It is clear and allows the reader understanding the main concepts behind models for longitudinal data analysis, with few effort from a technical viewpoint. The examples used to illustrate the methods covered in the textbook are numerous and also rather easy to follow. This helps the reader learn how to proceed with a full longitudinal data analysis."
Maria Francesca Marino, University of Florence, Italy, The American Statistician, February 2024.
"Overall, this book is a very comprehensive coverage of the methods for analysing longitudinal data with missing observations. For those of us who teach or supervise students and researchers in the application of linear regression models, this is a useful resource. One of the most notable aspects of the book is the wealth of exercises. The book's companion website provides the data files for working through the exercises. If you are looking for a textbook that explains the material through worked examples and exercises, then you have found a real gem. Similarly, if you are a practising biostatistician with a particularly developed understanding of the nuances of longitudinal data analysis, you will find it of academic interest."
Pentti Nieminen, Finland, ISCB News, May 2024.
