| Description |
1 online resource (506 pages) |
| Contents |
Introduction A Motivating Example Definition of Missing Data Missing Data Patterns Missing Data Mechanisms Structure of the Book Statistical Background Introduction Frequentist Theory Sampling Experiment Model, Parameter, and Estimation Hypothesis Testing Resampling Methods: the Bootstrap Approach Bayesian Analysis Rudiments Prior Distribution Bayesian Computation Asymptotic Equivalence between Frequentist and BayesianEstimates Likelihood-Based Approaches to Missing Data Analysis Ad-Hoc Missing Data Methods Use of Monte Carlo Simulation Study Summary Multiple Imputation Analysis: Basics Introduction Basic Ideas Bayesian Motivation Basic Combining Rules and Their Justifications Why Does Multiple Imputation Work Statistical Inference on Multiply Imputed Data Scalar Inference Multi-Parameter Inference How to Choose the Number of Imputations How to Create Multiple Imputations Bayesian Imputation algorithm Proper Multiple Imputation Alternative Strategies Practical Implementation Summary Multiple Imputation for Univariate Missing Data: Parametric Methods Overview Imputation for Continuous Data Based on Normal Linear ModelsImputation for Non-Continuous Data Based on GeneralizedLinear Models Generalized Linear Models Imputation for Binary Data Logistic Regression Model Imputation Discriminant Analysis Imputation Rounding Data Separation Imputation for Non-Binary Categorical Data Imputation for Other Types of Data Imputation for a Missing Covariate in a Regression Analysis Summary Multiple Imputation for Univariate Missing Data: Robust Methods Overview Data Transformation Transforming or Not How to Apply Transformation in Multiple Imputation Imputation Based on Smoothing Methods Main Idea Practical Use Adjustments for Continuous Data with Range Restrictions Predictive Mean Matching Hot-Deck Imputation Basic Idea and Procedure PMM for Non-Continuous Data Additional Discussion Inclusive Imputation Strategy Basic Idea Dual Modeling Strategy Propensity Score Calibration Estimation and Doubly RobustImputation Methods Summary Multiple Imputation for Multivariate Missing Data: the Joint Modeling Approach Introduction Imputation for Monotone Missing Data Multivariate Continuous Data Multivariate Normal Models Nonnormal Continuous Data Multivariate Categorical Data Log-Linear Models Latent Variable Models Mixed Categorical and Continuous Variables One Continuous Variable and One Binary Variable General Location Models Latent Variable Models Missing Outcome and Covariates in a Regression Analysis General Strategy Conditional Modeling Framework Using WinBUGS Background Missing Interactions and Squared Terms of Covariates in Regression Analysis Imputation Using Flexible Distributions Summary Multiple Imputation for Multivariate Missing Data: the Fully Conditional Specification Approach Introduction Basic Idea Specification of Conditional Models Handling Complex Data Features Data Subject to Bounds or Restricted Ranges Skip Patterns Implementation General Algorithm Software Using WinBUGS Subtle Issues Compatibility Performance under Model Misspecifications A Practical Example Summary Multiple Imputation in Survival Data Analysis Introduction Imputation for Censored Data Theoretical Basis Parametric Imputation for Censored Event Times Semiparametric Imputation for Censored Event Times Merits of Imputing Censored Event Times Survival Analysis with Missing Covariates Overview Joint Modeling Fully Conditional Specification Semiparametric Methods Summary Multiple Imputation for Longitudinal Data Introduction Mixed Models for Longitudinal Data Imputation Based on Mixed Models Why Using Mixed Models General Imputation Algorithm Examples Wide Format Imputation Multilevel Data Summary Multiple Imputation Analysis for Complex Survey Data Introduction Design-Based Inference for Survey Data Imputation Strategies for Complex Survey Data General Principles Incorporating the Survey Sampling Design Assuming MAR Using FCS Modeling Options Some Examples from the Literature Database Construction and Release Data Editing Documentation and Release Summary Multiple Imputation for Data Subject to Measurement Error Introduction Rationale Imputation Strategies True Values Partially Observed Basic Setup Direct Imputation Accommodating a Specific Analysis Using FCS Predictors under Detection Limits True Values Fully Unobserved Data Harmonization Using Bridge Studies Combining Information from Multiple Data Sources Imputation for a Composite Variable Summary Multiple Imputation Diagnostics Overview Imputation Model Development Inclusion of Variables Forming Imputation Models Comparison between Observed and Imputed Values Comparison on Marginal Distributions Comparison on Conditional Distributions Basic Idea Using Propensity Score Checking Completed Data Posterior Predictive Checking Comparing Completed Data with Their Replicates Assessing the Fraction of Missing Information Relating the Fraction of Missing Information with Model Predictability Prediction Accuracy Comparison among Different Methods Summary Multiple Imputation Analysis for Nonignorable Missing Data Introduction The Implication of Missing Not at Random Using the Inclusive Imputation to Rescue Missing Not at Random Models Selection Models Pattern Mixture Models Shared Parameter Models Analysis Strategies Direct Imputation Sensitivity Analysis Summary Some Advanced Topics Overview Uncongeniality in Multiple Imputation Analysis Combining Analysis Results from Multiply Imputed Datasets:Further Considerations Normality Assumption in Question Beyond Sufficient Statistics Complicated Completed-Data Analyses: Variable SelectionHigh-Dimensional Data Final Thoughts |
| Summary |
Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis. Over the past 40 years or so, multiple imputation has gone through rapid development in both theories and applications. It is nowadays the most versatile, popular, and effective missing-data strategy that is used by researchers and practitioners across different fields. There is a strong need to better understand and learn about multiple imputation in the research and practical community. Accessible to a broad audience, this book explains statistical concepts of missing data problems and the associated terminology. It focuses on how to address missing data problems using multiple imputation. It describes the basic theory behind multiple imputation and many commonly-used models and methods. These ideas are illustrated by examples from a wide variety of missing data problems. Real data from studies with different designs and features (e.g., cross-sectional data, longitudinal data, complex surveys, survival data, studies subject to measurement error, etc.) are used to demonstrate the methods. In order for readers not only to know how to use the methods, but understand why multiple imputation works and how to choose appropriate methods, simulation studies are used to assess the performance of the multiple imputation methods. Example datasets and sample programming code are either included in the book or available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book). Key Features Provides an overview of statistical concepts that are useful for better understanding missing data problems and multiple imputation analysis Provides a detailed discussion on multiple imputation models and methods targeted to different types of missing data problems (e.g., univariate and multivariate missing data problems, missing data in survival analysis, longitudinal data, complex surveys, etc.) Explores measurement error problems with multiple imputation Discusses analysis strategies for multiple imputation diagnostics Discusses data production issues when the goal of multiple imputation is to release datasets for public use, as done by organizations that process and manage large-scale surveys with nonresponse problems For some examples, illustrative datasets and sample programming code from popular statistical packages (e.g., SAS, R, WinBUGS) are included in the book. For others, they are available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book) |
| Notes |
Yulei He and Guangyu Zhang are mathematical statisticians at the National Center for Health Statistics, the U.S. Centers for Disease Control and Prevention. Chiu-Heish Hsu is a Professor of Biostatistics at the University of Arizona. All authors have researched, taught, and consulted in multiple imputation and missing data analysis in the past 20 years |
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Vendor-supplied metadata |
| Subject |
Missing observations (Statistics)
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Multiple imputation (Statistics)
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MATHEMATICS -- Probability & Statistics -- General.
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Missing observations (Statistics)
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Multiple imputation (Statistics)
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| Form |
Electronic book
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| Author |
Zhang, Guangyu (Statistician), author.
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Hsu, Chiu-Hsieh, author
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| ISBN |
9780429156397 |
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0429156391 |
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9780429545672 |
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0429545673 |
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9780429530975 |
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0429530978 |
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9781498722070 |
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1498722075 |
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