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

Michael J. Daniels, Joseph W. Hogan

Preface. Description of Motivating Examples. Regression Models. Methods of Bayesian Inference. Bayesian Analysis Using Data on Completers. Missing Data Mechanisms and Longitudinal Data. Inference about Full-Data Parameters under Ignorability. Case Studies: Ignorable Missingness. Modelsfor handling Nonignorable Missingness. Informative Priors and Sensitivity Analysis. Case Studies: Model Specification and Data Analysis under Missing Not at Random. Appendix. Bibliography. Index.