Applied Linear Regression for Longitudinal Data

With an Emphasis on Missing Observations

Frans E.S. Tan, Shahab Jolani

• Publisher: Chapman and Hall/CRC
• Year: 2022
• Print ISBN: 9780367634315
• eBook ISBN: 9781000798258
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Key features include the following:

• Provides datasets and examples online
• Gives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysis
• Conceptualises the analysis of comparative (experimental and observational) studies

It is the ideal companion for researchers and students in epidemiological, health, and social and behavioral sciences working with longitudinal studies without a mathematical background.

Table of contents

• Cover Page
• Half Title page
• Series Page
• Title Page
• Copyright Page
• Dedication
• Contents
• Preface
• Acknowledgements
• Short Description of Research and Simulation Studies
• 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
• References
• Index