Book description
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 modern 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 methods and the field of diagnostic medicine.
Organized by types of data, chapter coverage begins with an overall introduction to the existence and limitations of missing data and continues into traditional techniques for missing data inference, including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods. The book's 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 the SAS, Stata, R, and WinBUGS software packages
Numerous examples of case studies in the field of biostatistics 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
- 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
- 5.1 Overview
- 5.2 Examples
- 5.3 Longitudinal Regression Models for Complete Data
- 5.4 Missing Data Settings and Simple Methods
- 5.5 Likelihood Approach
- 5.6 Inverse Probability Weighted GEE with MAR Dropout
- 5.7 Extension to Nonmonotone Missingness
- 5.8 Multiple Imputation
- 5.9 Bayesian Inference
- 5.10 Other Approaches
- Appendix 5.A: Technical Details of the Approximation Methods for GLMM and Computer Code for the Examples
- Chapter 6: Survival Analysis under Ignorable Missingness
- Chapter 7: Nonignorable Missingness
-
Chapter 8: Analysis of Randomized Clinical Trials with Noncompliance
- 8.1 Overview
- 8.2 Examples
- 8.3 Some Common but Naive Methods
- 8.4 Notations, Assumptions, and Causal Definitions
- 8.5 Method of Instrumental Variables
- 8.6 Moment-based Method
- 8.7 Maximum Likelihood and Bayesian Methods
- 8.8 Noncompliance and Missing Outcome Data
- 8.9 Analysis of the Two Examples
- 8.10 Other Methods for Dealing with both Noncompliance and Missing Data
- Appendix 8.A: Multivariate Delta Method
- Bibliography
- Index
- End User License Agreement
Product information
- Title: Applied Missing Data Analysis in the Health Sciences
- Author(s):
- Release date: June 2014
- Publisher(s): Wiley
- ISBN: 9780470523810
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