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Applied Missing Data Analysis in the Health Sciences

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

    1. Cover
    2. Wiley Series in Statistics in Practice
    3. Title Page
    4. Copyright
    5. Dedication
    6. List of Figures
    7. List of Tables
    8. Preface
    9. Chapter 1: Missing Data Concepts and Motivating Examples
      1. 1.1 Overview of the Missing Data Problem
      2. 1.2 Patterns and Mechanisms of Missing Data
      3. 1.3 Data Examples
    10. Chapter 2: Overview of Methods for Dealing with Missing Data
      1. 2.1 Methods that Remove Observations
      2. 2.2 Methods that Utilize all Available Data
      3. 2.3 Methods that Impute Missing Values
      4. 2.4 Bayesian Methods
    11. Chapter 3: Design Considerations in the Presence of Missing Data
      1. 3.1 Design Factors Related to Missing Data
      2. 3.2 Strategies for Limiting Missing Data in the Design of Clinical Trials
      3. 3.3 Strategies for Limiting Missing Data in the Conduct of Clinical Trials
      4. 3.4 Minimize the Impact of Missing Data
    12. Chapter 4: Cross-sectional Data Methods
      1. 4.1 Overview of General Methods
      2. 4.2 Data Examples
      3. 4.3 Maximum Likelihood Approach
      4. 4.4 Bayesian Methods
      5. 4.5 Multiple Imputation
      6. 4.6 Imputing Estimating Equations
      7. 4.7 Inverse Probability Weighting
      8. 4.8 Doubly Robust Estimators
      9. 4.9 Code Used in This Chapter
    13. Chapter 5: Longitudinal Data Methods
      1. 5.1 Overview
      2. 5.2 Examples
      3. 5.3 Longitudinal Regression Models for Complete Data
      4. 5.4 Missing Data Settings and Simple Methods
      5. 5.5 Likelihood Approach
      6. 5.6 Inverse Probability Weighted GEE with MAR Dropout
      7. 5.7 Extension to Nonmonotone Missingness
      8. 5.8 Multiple Imputation
      9. 5.9 Bayesian Inference
      10. 5.10 Other Approaches
      11. Appendix 5.A: Technical Details of the Approximation Methods for GLMM and Computer Code for the Examples
    14. Chapter 6: Survival Analysis under Ignorable Missingness
      1. 6.1 Overview
      2. 6.2 Introduction
      3. 6.3 Enhanced Complete-Case Analysis
      4. 6.4 Weighted Methods
      5. 6.5 Imputation Methods
      6. 6.6 Nonparametric Maximum Likelihood Estimation
      7. 6.7 Transformation Model
      8. 6.8 Data Example: Pathways Study
      9. 6.9 Concluding Remarks
    15. Chapter 7: Nonignorable Missingness
      1. 7.1 Introduction
      2. 7.2 Cross-Sectional Data: Selection Model
      3. 7.3 Longitudinal Data with Dropout
      4. 7.4 Bayesian Analysis for Generalized Linear Models with Nonignorably Missing Covariates
      5. 7.5 Multiple Imputation
      6. 7.6 Inverse Probability Weighted Methods
    16. Chapter 8: Analysis of Randomized Clinical Trials with Noncompliance
      1. 8.1 Overview
      2. 8.2 Examples
      3. 8.3 Some Common but Naive Methods
      4. 8.4 Notations, Assumptions, and Causal Definitions
      5. 8.5 Method of Instrumental Variables
      6. 8.6 Moment-based Method
      7. 8.7 Maximum Likelihood and Bayesian Methods
      8. 8.8 Noncompliance and Missing Outcome Data
      9. 8.9 Analysis of the Two Examples
      10. 8.10 Other Methods for Dealing with both Noncompliance and Missing Data
      11. Appendix 8.A: Multivariate Delta Method
    17. Bibliography
    18. Index
    19. End User License Agreement