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Handbook of Missing Data Methodology (Chapman & Hall/CRC Handbooks of Modern Statistical Methods)

Handbook of Missing Data Methodology (Chapman & Hall/CRC Handbooks of Modern Statistical Methods) - Free Ebook Download

Book Detail

Author/Editor(s): Geert Molenberghs, Garrett Fitzmaurice, Michael G. Kenward, Anastasios Tsiatis, Geert Verbeke
Publication Date: November 6, 2014
ISBN-10: 1439854610
ISBN-13: 978-1439854617
Language: English
Edition: 1
Publisher: Chapman and Hall/CRC
Size: 10.4 MB
Format: pdf

Book Description

Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research.

Divided into six parts, the handbook begins by establishing notation and terminology. It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods.

The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative, routinely non-verifiable assumptions about the missing data process. The final part discusses special topics, such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part, an introduction provides useful background material and an overview to set the stage for subsequent chapters.

Covering both established and emerging methodologies for missing data, this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.

About the Author

Geert Molenberghs is a Professor of Biostatistics at Universiteit Hasselt and Katholieke Universiteit Leuven in Belgium. He earned a B.S. degree in mathematics (1988) and a Ph.D. in biostatistics (1993) from Universiteit Antwerpen. He has published on surrogate markers in clinical trials, and on categorical, longitudinal, and incomplete data. He was joint editor of Applied Statistics (2001–2004), co-editor of Biometrics (2007–2009), and co-editor of Biostatistics (2010–2015). He was president of the International Biometric Society (2004–2005), received the Guy Medal in Bronze from the Royal Statistical Society and the Myrto Lefkopoulou award from the Harvard School of Public Health. Geert Molenberghs is founding director of the Center for Statistics. He is also the director of the Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat). Jointly with Geert Verbeke, Mike Kenward, Tomasz Burzykowski, Marc Buyse, and Marc Aerts, he has authored books on longitudinal and incomplete data, and on surrogate marker evaluation. Geert Molenberghs has received several Excellence in Continuing Education Awards of the American Statistical Association, for courses at Joint Statistical Meetings. Geert Molenberghs is a member of the Belgian Royal Academy of Medicine. He received accreditation as a professional statistician by the American Statistical Association (2011–2017).

Garrett Fitzmaurice is a Professor of Psychiatry (biostatistics) at the Harvard Medical School and a Professor in the Department of Biostatistics at the Harvard School of Public Health. He is a Fellow of the American Statistical Association and a member of the International Statistical Institute. He has served as Associate Editor for Biometrics, Journal of the Royal Statistical Society, Series B, and Biostatistics and as Statistics Editor for the journal Nutrition. His research and teaching interests are in methods for analyzing longitudinal and repeated measures data. A major focus of his methodological research has been on the development of statistical methods for analyzing repeated binary data and for handling the problem of attrition in longitudinal studies. Much of his collaborative research has concentrated on applications to mental health research, broadly defined. He co-authored the textbook Applied Longitudinal Analysis, 2nd edition (Wiley, 2011) and co-edited the handbook Longitudinal Data Analysis (Chapman & Hall/CRC Press, 2009).

Mike Kenward has been GSK Professor of Biostatistics at the London School of Hygiene and Tropical Medicine since 1999, with former positions at the Universities of Kent and Reading in the UK, and research institutes in the UK, Iceland, and Finland. His main research interests are in the analysis of longitudinal data, cross-over trials, small sample inference in REML, and the problem of missing data. He has co-authored three textbooks: The Design and Analysis of Cross-Over Trials with Byron Jones, Missing Data in Clinical Studies with Geert Molenberghs, and Multiple Imputation and Its Application with James Carpenter. He has been a co-editor of Biometrics, and is currently an associate editor of Biostatistics. During the past 25 years he has acted as a consultant in biostatistics, largely for the pharmaceutical industry, and he has given over 120 short courses worldwide on various topics in biostatistics, and has appeared as an expert witness in statistics in the U.S. Federal District Court of New York. He has recently completed the third Edition of The Design and Analysis of Cross-Over Trials with Byron Jones.

Anastasios A. Tsiatis is the Gertrude M. Cox Distinguished Professor of Statistics at North Carolina State University. He earned a B.S. degree in mathematics (1970) from the Massachusetts Institute of Technology and a PhD in statistics (1974) from the University of California at Berkeley. His research has focused on developing statistical methods for the design and analysis of clinical trials, censored survival analysis, group sequential methods, surrogate markers, semi-parametric methods with missing and censored data, causal inference and dynamic treatment regimes and has been the major PhD advisor for more than 40 students working in these areas. He is Co-Editor of Biostatistics (2010–2015) and has authored a book, entitled: Semiparametric Theory and Missing Data. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and is the recipient of the Spiegelman Award, the Snedecor Award, and the Princess Lilian Visiting Professorship in Belgium. He holds an honorary doctorate from Hasselt University in Belgium.

Geert Verbeke is a Professor in Biostatistics at Katholieke Universiteit Leuven and Universiteit Hasselt in Belgium. He earned a B.S. degree in mathematics (1989) from the Katholieke Universiteit Leuven, an M.S. in biostatistics (1992) from Universiteit Hasselt, and earned a Ph.D. in biostatistics (1995) from the Katholieke Universiteit Leuven. He has published extensively on various aspects of mixed models for longitudinal data analyses about which he co-authored and co-edited several textbooks (Springer Lecture Notes 1997; Springer Series in Statistics 2000 and 2005; Chapman & Hall/CRC 2009). He has held visiting positions at the Gerontology Research Center and Johns Hopkins University (Baltimore, MD), was International Program Chair for the International Biometric Conference in Montreal (2006), joint editor of the Journal of the Royal Statistical Society, Series A (2005–2008), and co-editor of Biometrics (2010–2012). He has served on a variety of committees of the International Biometric Society, is an elected Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. He was elected international representative on the Board of Directors of the American Statistical Association (2008–2010) and council member of the Royal Statistical Society (2013–2016). Geert Verbeke earned Excellence in Continuing Education Awards in 2002, 2004, 2008, and 2011 for short courses taught at the Joint Statistical Meetings of the American Statistical Association. He received the International Biometric Society Award for the best Biometrics paper in 2006 and received accreditation as professional statistician by the American Statistical Association (2010–2016).
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