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Multiple Imputation and Its Application

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ISBN-10: 0470740523

ISBN-13: 9780470740521

Edition: 2011

Authors: James Carpenter, Michael Kenward

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Imputation is the substitution of some value for a missing data point or a missing component of a data point. Once all missing values have been imputed, the dataset can then be analysed using standard techniques for complete data. This book is written with three main aims; to provide a thorough introduction to the general MI methods, to provide a detailed discussion of the practical use of the MI method and to present real-world examples drawn from the field of biostatistics. Illustrated throughout, using different issues that arise in the use of MI in observational and clinical trial settings. Relevant computer code and data will be provided for the examples used throughout the book and…    
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Book details

Copyright year: 2011
Publisher: John Wiley & Sons, Limited
Publication date: 1/18/2013
Binding: Hardcover
Pages: 364
Size: 6.00" wide x 9.25" long x 1.00" tall
Weight: 0.682
Language: English

Data acknowledgements
Reasons for missing data
Patterns of missing data
Consequences of missing data
Inferential framework and notation
Missing Completely At Random (MCAR)
Missing At Random (MAR)
Missing Not At Random (MNAR)
Using observed data to inform assumptions about the missingness mechanism
Implications of missing data mechanisms for regression analyses
Partially observed response
Missing covariates
Missing covariates and response
Subtle issues I: The odds ratio
Implication for linear regression
Subtle issues II: Subsample ignorability
Summary: When restricting to complete records is valid
The multiple imputation procedure and its justification
Intuitive outline of the MI procedure
The generic MI procedure
Bayesian justification of MI
Frequentist inference
Large number of imputations
Small number of imputations
Choosing the number of imputations
Some simple examples
MI in more general settings
Survey sample settings
Constructing congenial imputation models
Practical considerations for choosing imputation models
Multiple Imputation for Cross Sectional Data
Multiple imputation of quantitative data
Regression imputation with a monotone missingness pattern
MAR mechanisms consistent with a monotone pattern
Joint modelling
Fitting the imputation model
Full conditional specification
Full conditional specification versus joint modelling
Software for multivariate normal imputation
Multiple imputation of binary and ordinal data
Sequential imputation with monotone missingness pattern
Joint modelling with the multivariate normal distribution
Modelling binary data using latent normal variables
Latent normal model for ordinal data
General location model
Full conditional specification
Issues with over-fitting
Pros and cons of the various approaches
Multiple imputation of unordered categorical data
Monotone missing data
Multivariate normal imputation for categorical data
Maximum indicant model
Continuous and categorical variable
Imputing missing data
More than one categorical variable
General location model
FCS with categorical data
Perfect prediction issues with categorical data
Nonlinear relationships
Passive imputation
No missing data in nonlinear relationships
Missing data in nonlinear relationships
Predictive Mean Matching (PMM)
Just Another Variable (JAV)
Joint modelling approach
Extension to more general models and missing data patterns
Metropolis-Hastings sampling
Rejection sampling
FCS approach
Interaction variables rally observed
Interactions of categorical variables
General nonlinear relationships
Advanced Topics
Survival data, skips and large datasets
Time-to-event data
Imputing missing covariate values
Survival data as categorical
Imputing censored survival times
Nonparametric, or 'hot deck' imputation
Nonparametric imputation for survival data
Multiple imputation for skips
Two-stage MI
Large datasets
Large datasets and joint modelling
Shrinkage by constraining parameters
Comparison of the two approaches
Multiple imputation and record linkage
Measurement error
Multiple imputation for aggregated scores
Multilevel multiple imputation
Multilevel imputation model
MCMC algorithm for imputation model
Imputing level-2 covariates using FCS
Individual patient meta-analysis
When to apply Rubin's rules
Random level-1 covariance matrices
Model fit
Sensitivity analysis: MI unleashed
Review of MNAR modelling
Framing sensitivity analysis
Pattern mixture modelling with MI
Missing covariates
Application to survival analysis
Pattern mixture approach with longitudinal data via MI
Change in slope post-deviation
Piecing together post-deviation distributions from other trial arms
Approximating a selection model by importance weighting
Algorithm for approximate sensitivity analysis by re-weighting
Including survey weights
Using model based predictions
Bias in the MI variance estimator
MI with weights
Estimation in domains
A multilevel approach
Further developments
Robust multiple imputation
Theoretical background
Simple estimating equations
The Probability Of Missingness (POM) model
Augmented inverse probability weighted estimating equation
Robust multiple imputation
Univariate MAR missing data
Longitudinal MAR missing data
Simulation studies
Univariate MAR missing data
Longitudinal monotone MAR missing data
Longitudinal nonmonotone MAR missing data
Nonlongitudinal nonmonotone MAR missing data
Results and discussion
The RECORD study
Markov Chain Monte Carlo
Probability distributions
Posterior for the multivariate normal distribution
Index of Authors
Index of Examples