Missing Data in Longitudinal Studies

Discover the essential insights of Missing Data in Longitudinal Studies by Michael J. Daniels, published by Taylor & Francis Inc in 2008. This comprehensive hardback edition spans 324 pages and provides a unified Bayesian approach to effectively manage missing data in longitudinal studies.

Delve into real-world examples and case studies, including those focused on aging and HIV, which illustrate the practical applications of the methodologies discussed. The book thoroughly examines critical assumptions such as Missing At Random (MAR) and ignorability, emphasizing the significance of covariance modeling in the presence of incomplete data. Additionally, it explores mixture and selection models for addressing nonignorable missingness.

This authoritative resource is a must-have for researchers and practitioners in biostatistics, mathematics, and statistics, offering valuable techniques for handling one of the most challenging aspects of longitudinal research.