This book focuses on how to handle missing data in longitudinal studies, offering specific coverage of models for longitudinal data, missing data mechanisms, and various approaches to sensitivity analysis. It presents an overview of state-of-the-art methods for dealing with missing data, with particular emphasis on handling dropout and causal inference. Many examples, case studies, and applications from the medical sciences support the discussions. The authors use WinBUGS and R to execute the methods and provide datasets and code for download from the Internet. This book stands apart by virtue of the authors' Bayesian approach to inference along with their emphasis on missing data.
Daniels and Hogan's is the first to explicitly focus on missing data in the context of longitudinal studies. ! I found the book extremely clear and illuminating. It is well written, with comprehensive and up-to-date references. The use of example datasets from a number of epidemiological and clinical studies illustrates how the methods and strategies being advocated can be applied in real-life settings. ! an extremely valuable resource both to applied statisticians who are faced with analyzing longitudinal data subject to missingness and methodological researchers in the area. --Jonathan Bartlett, Statistics in Medicine, 2011, 30 ! They [the authors] have gone further than anyone else in developing methods for the not missing at random (NMAR) case. ! The focus on longitudinal studies will attract many readers. ! this book is an excellent introduction and is also a first-rate treatment of cutting-edge topics. ! --Paul D. Allison, University of Pennsylvania, Significance, September 2010 This text is the only Bayesian textbook that provides a contemporary and comprehensive treatment of Bayesian approaches to a common and critically important topic. The authors provide a scholarly treatment of Bayesian inference and supplement their treatise with concrete practical examples. The writing is clear, precise and interesting. A particularly innovative and enormously useful contribution is the authors' formalization of sensitivity analyses. They distinguish between local and global sensitivity analyses, providing the reader with examples of each. I have used the techniques proposed in the text with much success, teaching people the importance of separating what is observed from what is assumed. I strongly endorse this book. --Sharon-Lise Normand, Harvard School of Public Health, Boston, Massachusetts, USA !the book under review appears to be the first reference that solely focuses on Bayesian approaches to handle missing data in longitudinal studies. ! Overall I think this is a well-written technical monograph. The preliminary sections on longitudinal data analysis, Bayesian statistics, and missing data ! are well written and serve to make this book a self-contained reference. The models presented to analyze missing data in longitudinal studies cover many ideas from the current literature, and some of the methods are at the cutting edge of research. The book will probably have greatest appeal to statisticians with a research interest in missing data. Although I also think applied biostatisticians who like to use Bayesian approaches and in particular WinBUGS will find this book very useful. --Journal of Biopharmaceutical Statistics, 2009 !a timely and thorough review of this maturing research area. ! The book is comprehensive in covering models for both continuous and discrete outcomes from both the pattern mixture and selection modeling perspectives. ! The book's composition offers much to admire. The writing is clear and direct, the notation is sensible and consistent, and tables and figures are simple and uncluttered. Typos are mercifully rare ! Biostatisticians who seek a clear and thorough overview of the state of knowledge in this area would do well to make this excellent book their first stop. --Biometrics, March 2009
REGRESSION AND INFERENCE: Datasets. Regression Models, Bayesian inference . Data Analysis . MISSING DATA: Missing data mechanisms. Inference under MAR. Model-based inference about full data under missing not at random. Application of missing datamethods to problems in causal inference. Sensitivity analysis. Notation. References.
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