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
"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
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