Flexible Imputation of Missing Data

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