Applied Missing Data Analysis in the Health Sciences

Xiao-Hua Zhou, Chuan Zhou, Danping Lui, Xaiobo Ding

• Publisher: Wiley
• Year: 2014
• Print ISBN: 9780470523810
• eBook ISBN: 9781118573648
• Edition: 1
• Topic: Medicina
• Subtopic: Probabilità e statistica

Applied Missing Data Analysis in the Health Sciences

A modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics

With an emphasis on hands-on applications, Applied Missing Data Analysis in the Health Sciences outlines the various statistical methods for the analysis of missing data. The authors acknowledge the limitations of established techniques and provide newly-developed methods with concrete applications in areas such as causal inference.

Organized by types of data, chapter coverage begins with an overall introduction to the existence and limitations of missing data and continues into techniques for missing data inference, including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods. The book subsequently covers cross-sectional, longitudinal, hierarchical, survival data. In addition, Applied Missing Data Analysis in the Health Sciences features:

• Multiple data sets that can be replicated using SAS®, Stata®, R, and WinBUGS software packages
• Numerous examples of case studies to illustrate real-world scenarios and demonstrate applications of discussed methodologies
• Detailed appendices to guide readers through the use of the presented data in various software environments

Applied Missing Data Analysis in the Health Sciences is an excellent textbook for upper-undergraduate and graduate-level biostatistics courses as well as an ideal resource for health science researchers and applied statisticians.

Chapter 1
Missing Data Concepts and Motivating Examples

1.1 Overview of the Missing Data Problem

Data are the fundamental building blocks of valid statistical inference for biomedical and social sciences research. Unfortunately, for many reasons, more often than not we will be missing some observations. Data are sometimes missing by design, such as in two-stage case-cohort designs. There are situations when missing data are not relevant to the analysis and therefore can be safely ignored. So, it is important to understand what we mean by missing data in this book. According to Little et al. (2012b, missing data are defined as values that are not available, but otherwise would be meaningful for analysis if they were observed. Even in the case of missing data, the goal remains to make inferences about the population targeted by the complete sample. Unfortunately, there is no universal method for handling a missing data problem. This is because the selection of subjects for a study is usually known, but the process by which observations on those subjects become missing—the missingness mechanism—is usually unknown, and the data alone cannot definitively inform us about this process. Therefore, with missing data, additional assumptions are required in order to proceed with analysis, and the validity of these assumptions cannot be determined from the observed data alone. For this reason, assessing the sensitivity of conclusions to the assumptions should play a central role in any analysis of data with missing values. In fact, any analysis should principally include the hypothesis under investigation, the information on the observed data, and the reasons for the missing data. When data are missing, information is lost, and the value of what remains depends on whether we can identify plausible reasons for missing the data and on the sensitivity of the study conclusions to different assumptions on missingness mechanisms.

Over the years, our view on missing data problems has evolved substantially as we have obtained new insights into the problem and learned to deal with it. In the beginning, missing values were merely an inconvenience as holes in a data matrix that cause statistical software to crash. Therefore, early work on the problem was largely on computation (Afifi and Elashoff, 1966; Dempster et al., 1977). Later we realized that 3 even if data were not there, information could still be obtained from the way data were missing or from the relationship between the missing and observed values. This led to the missingness mechanism categorization and imputation based on auxiliary variables. We found that augmenting the observed data with the extracted additional information, along with appropriate inference adjustment, can improve the statistical analysis (Little and Rubin, 2002; Schafer, 1997b). Later researchers further realized that missing values are part of a more general concept of coarsened data, which include numbers that have been grouped, aggregated, rounded, censored, or truncated, resulting in partial loss of information (Heitjan and Rubin, 1991). Missing data methods may be extended to a broader research on analysis of partially observed data that encompassed a wide range of advanced statistical topics such as double robustness, causal inference, and semiparametric theory (Robins and Rotnitzky, 1995; Tsiatis, 2006).

The statistical literature on the missing data problem is now quite extensive with many excellent textbooks available. Little and Rubin (2002) provided a good overview of likelihood methods and an introduction to multiple imputation. Allison (2001) presented a less technical overview intended for technically less sophi...

Table of contents

• Cover
• Wiley Series in Statistics in Practice
• Title Page
• Copyright
• Dedication
• List of Figures
• List of Tables
• Preface
• Chapter 1: Missing Data Concepts and Motivating Examples
• Chapter 2: Overview of Methods for Dealing with Missing Data
• Chapter 3: Design Considerations in the Presence of Missing Data
• Chapter 4: Cross-sectional Data Methods
• Chapter 5: Longitudinal Data Methods
• Chapter 6: Survival Analysis under Ignorable Missingness
• Chapter 7: Nonignorable Missingness
• Chapter 8: Analysis of Randomized Clinical Trials with Noncompliance
• Bibliography
• Index
• End User License Agreement