Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies

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Yulei He Guangyu Zhang

Multiple Imputation of Missing Data in Practice

Basic Theory and Analysis Strategies

Yulei He, Guangyu Zhang, Chiu-Hsieh Hsu (Epidemiology and Biostatistics Division, University of Arizona, USA)

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English

Chapman & Hall/CRC
27 May 2024

Probability & statistics

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)

By: Yulei He, Guangyu Zhang, Chiu-Hsieh Hsu (Epidemiology and Biostatistics Division, University of Arizona, USA)
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
ISBN: 9781032136899
ISBN 10: 1032136898
Pages: 476
Publication Date: 27 May 2024
Audience: General/trade , ELT Advanced
Format: Paperback
Publisher's Status: Forthcoming

1. Introduction. 2. Statistical Background. 3. Multiple Imputation Analysis: Basics. 4. Multiple Imputation for Univariate Missing Data: Parametric Methods. 5. Multiple Imputation for Univariate Missing Data: Robust Methods. 6. Multiple Imputation for Multivariate Missing Data: the Joint Modeling Approach. 7. Multiple Imputation for Multivariate Missing Data: the Fully Conditional Specification Approach. 8. Multiple Imputation in Survival Data Analysis. 9. Multiple Imputation for Longitudinal Data. 10. Multiple Imputation Analysis for Complex Survey Data. 11. Multiple Imputation for Data Subject to Measurement Error. 12. Multiple Imputation Diagnostics.

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.