Flexible Imputation of Missing Data: (Chapman & Hall/CRC Interdisciplinary Statistics)

A practical guide for handling missing data, this book provides a flexible and accessible framework for multiple imputation along with strategies for obtaining effective solutions to these problems. The text is supported by many examples using real data taken from the author's vast research involving missing data. All of the analyses can be replicated in R using the dedicated package MICE, which was developed by the author. Topics covered include multiple imputation, multivariate and univariate missing data, as well as issues of measurement, selection, and longitudinal data.

Table of Contents:
Basics Introduction The problem of missing data Concepts of MCAR, MAR and MNAR Simple solutions that do not (always) work Multiple imputation in a nutshell Goal of the book What the book does not cover Structure of the book Exercises Multiple imputation Historic overview Incomplete data concepts Why and when multiple imputation works Statistical intervals and tests Evaluation criteria When to use multiple imputation How many imputations? Exercises Univariate missing data How to generate multiple imputations Imputation under the normal linear normal Imputation under non-normal distributions Predictive mean matching Categorical data Other data types Classification and regression trees Multilevel data Non-ignorable methods Exercises Multivariate missing data Missing data pattern Issues in multivariate imputation Monotone data imputation Joint Modeling Fully Conditional Specification FCS and JM Conclusion Exercises Imputation in practice Overview of modeling choices Ignorable or non-ignorable? Model form and predictors Derived variables Algorithmic options Diagnostics Conclusion Exercises Analysis of imputed data What to do with the imputed data? Parameter pooling Statistical tests for multiple imputation Stepwise model selection Conclusion Exercises Case studies Measurement issues Too many columns Sensitivity analysis Correct prevalence estimates from self-reported data Enhancing comparability Exercises Selection issues Correcting for selective drop-out Correcting for non-response Exercises Longitudinal data Long and wide format SE Fireworks Disaster Study Time raster imputation Conclusion Exercises Extensions Conclusion Some dangers, some do's and some don'ts Reporting Other applications Future developments Exercises Appendices: Software R S-Plus Stata SAS SPSS Other software References Author Index Subject Index

Review :
"As an applied biostatistician, the introductory chapter spoke directly to me. It began motivating the issues in multiple imputation from the perspective of applied data problems and problematic approaches to them…Foundational examples start with simple scenarios that are gradually and clearly expanded upon. At each step, R code is shown and illustrations visually show the effects of different approaches. For every major concept there is a half-page "Algorithm Box", a short summary in pseudo-code of the algorithm being discussed. These, in conjunction with the explanatory text, made things extremely clear and easy to grasp… Overall, this book does an excellent job of bringing one from no knowledge of multiple imputation to a working knowledge of multiple imputation." —ISCB News, July 2016 "The opening chapters of this book will be useful to the newcomer to missing data, including the nonstatistician. Many of the recommendations in the `Do’s and don’ts’ section will be useful to the researcher who encounters missing data and wishes to deal with it responsibly. Finally, the code examples provide a reassuring companion to the user of the mice software package." —Biometrical Journal, 2014 "This book would be well suited as a textbook, especially at the graduate level, possibly for biostatisticians, epidemiologists, or applied scientists and users of statistical methodology. …a very enjoyable read, and—at least in my opinion—it is a book that belongs on everyone’s shelf as it does open one’s eyes to a problem that has surrounded us (and that many of us have ignored!) for a very long time." —Wolfgang S. Jank, Journal of the American Statistical Association, June 2013 "From the first lines of Chapter 1 throughout the entire monograph, the author presents numerous R language codes, so the book also serves as a good introduction to R. Each chapter is complete with various examples and exercises. The book is very useful to graduate students and researchers for solving practical problems with real data." —Technometrics, February 2013 "It’s excellent and I highly recommend it. … van Buuren’s book is great even if you don’t end up using the algorithm described in the book … he supplies lots of intuition, examples, and graphs." —Andrew Gelman, Columbia University "… a beautiful book that is so full of guidance for statisticians … exceptionally up to date and has more useful wisdom about dealing with common missing data problems than any other source I've seen." —Frank Harrell, Vanderbilt University "I’m delighted to see this new book on multiple imputation by Stef van Buuren …This book represents a 'no nonsense' straightforward approach to the application of multiple imputation. I particularly like Stef’s use of graphical displays … It’s great to have Stef’s book on multiple imputation, and I look forward to seeing more editions as this rapidly developing methodology continues to become even more effective at handling missing data problems in practice." —From the Foreword by Donald B. Rubin