This book focuses on how to handle missing data in longitudinal studies, offering specific coverage of models for longitudinal data, missing data mechanisms, and various approaches to sensitivity analysis. It presents an overview of state-of-the-art methods for dealing with missing data, with particular emphasis on handling dropout and causal inference. Many examples, case studies, and applications from the medical sciences support the discussions. The authors use WinBUGS and R to execute the methods and provide datasets and code for download from the Internet. This book stands apart by virtue of the authors' Bayesian approach to inference along with their emphasis on missing data.