Drawing from the authors' own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal studies. To illustrate these methods, the authors employ

chapter 1|14 pages

Description of Motivating Examples

chapter 2|24 pages

Regression Models for Longitudinal Data

chapter 3|33 pages

Methods of Bayesian Inference

chapter 4|13 pages

Worked Examples using Complete Data

chapter 7|20 pages

Case Studies: Ignorable Missingness

chapter 10|35 pages

Case Studies: Nonignorable Missingness