Tan F.E.S., Jolani S. Applied Linear Regression for Longitudinal data: With an Emphasis on Missing Observations

CRC Press, 2022. — 249 p. — (Texts in Statistical Science Series). — ISBN: 978-0-367-63937-2.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 visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputations are explained conceptually and the 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.
Conceptualizes the analysis of comparative (experimental and observational) studies.Special attention is given to the analysis of longitudinal intervention and life-event studies, where the objective is to evaluate a treatment or life-event effect. Several statistical methods to deal with missing observations are presented, depending on the type of missing data mechanism and whether the dependent variable (outcome), the independent variables (covariates) or both are partly missing.Chapter 1 introduces the scientific framework of linear regression analysis and the underlying theory of missing data methods. Chapter 2 starts with a brief review of the standard linear regression model, and the notation and terminology of multilevel linear models are introduced. In addition, this chapter reviews simple and advanced methods for handling missing observations. The material in Chapter 3 and Chapter 4 forms the heart of multilevel analysis. Various examples are used to introduce random effects and marginal models and to explain the steps of model building in longitudinal data. Suggestions are given on how to deal with missing data problems when considering imputation strategies. Chapter 5 compares the analysis of covariance (ANCOVA) and gain-score approach in pre/post-measurement designs. To address the problem of missing observations, sensitivity analysis via multiple imputations is demonstrated. Chapter 6 and Chapter 7 serve as case studies to perform a full analysis of longitudinal data in observational and experimental studies, respectively.