Description
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.
Table of Contents
1 Missing Data Concepts and Motivating Examples 1
2 Overview of Methods for Dealing with Missing Data 15
3 Design Considerations in the Presence Of Missing Data 25
4 Cross-sectional Data Methods 31
5 Longitudinal Data Methods 69
6 Survival Analysis Under Ignorable Missingness 121
7 Nonignorable Missingness 147
8 Analysis of Randomized Clinical Trials With Noncompliance 185
Bibliography 215
Index 225
