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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
Applied Linear Regression for Longitudinal Data
Modeling and Analysis of Longitudinal Data
Preventing and Treating Missing Data in Longitudinal Clinical Trials
Bayesian Nonparametrics for Causal Inference and Missing Data
Joint Modeling of Longitudinal and Time-to-Event Data
Longitudinal Data Analysis
Analysis of Longitudinal Data
Applied Longitudinal Data Analysis for Epidemiology
Applied Missing Data Analysis in the Health Sciences
Bayesian Inference for Partially Identified Models
Longitudinal Data Analysis
Antedependence Models for Longitudinal Data