Applied Linear Regression for Longitudinal Data : With an Emphasis on Missing Observations

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. "This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths, while at the same time not requiring much prior knowledge about regression analysis. 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 (in the outcome and/or independent variables) are clarified using visualization techniques. Key Features: Provides datasets and example online; Gives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysis; Conceptualize the analysis of comparative (experimental and observational) studies. It is the ideal companion for researcher and students in epidemiological, health, and social and behavioural sciences working with longitudinal studies without a mathematical background"-- Provided by publisher Netherlands, for facilitating an environment to write this book. We thank all colleagues and students who ever followed the MALD course and inspired us to even start this project. Their comments during and after the course were extremely useful.Special thanks to Miranda Janssen for developing the companion website and a close collaboration in this project. We wish to thank David Grubbs and Lara Spieker, Statistics Editors at Chapman & Hall/CRC for their encouragement and patience during this project. Jessica Poile and Curtis Hill have been very helpful in facilitation of this project. Finally, several people read and commented on parts of the book. Many thanks to Martijn Berger for reviewing the manuscript and providing many helpful suggestions. We also thank This book introduces best practices in longitudinal data analysis at an intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualising 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 visualisation techniques. Key features include the following: It is the ideal companion for researchers and students in epidemiological, health, and social and behavioural sciences working with longitudinal studies without a mathematical background.