Daniels Michael J., Hogan Joseph W. Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis

Chapman & Hall/CRC, 2007. — 324 p. — Monographs on Statistics & Applied Probability (Book 109). — ISBN: 978-1-58488-609-9.The book is intended for statisticians, data analysts and other scientists involved in the collection and analysis of longitudinal data. Our emphasis is on clinical and public health research, but many of the ideas will be applicable to other fields. We assume familiarity with statistical inference at the level of Casella and Berger (2001), and with regression at the level of Kutner et al. (2003). Familiarity with regression for longitudinal data and principles of Bayesian inference is helpful, but these ideas are reviewed in Chapters 2 and 3, where numerous references to books and key papers are given.Readers of this book are likely to have at least a passing familiarity with basic methods for handling missing data, but the literature is vast and there are distinct points of view on how to approach inference. Several outstanding texts can be consulted to gain all appreciation, including Little and Rubin (2002); Schafer (1997); van der Laan and Robins (2003); Tsiatis (2006); and Molenberghs and Kenward (2007).Although this is primarily a research monograph, we expect that it will be a suitable supplelnental text for graduate courses on longitudinal data, missing data, or Bayesian inference. We have used material from the book for our Own graduate courses at University of Florida and Brown University.Description of Motivating Examples.
Regression Models for Longitudinal Data.
Methods of Bayesian Inference.
Worked Examples using Complete Data.
Missing Data Mechanisms and Longitudinal Data.
Inference about Full-Data Parameters under Ignorability.
Case Studies: Ignorable Missingness.
Models for Handling Nonignorable Missingness.
Informative Priors and Sensitivity Analysis.
Case Studies: Nonignorable Missingness.
Distributions.Author Index.See also /file/708979/