Offers a unified Bayesian approach to handle missing data in longitudinal studies. This book contains examples and case studies on aging and HIV. It describes assumptions that include MAR and ignorability, demonstrate the importance of covariance modeling with incomplete data, and cover mixture and selection models for nonignorable missingness.