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.

Autorentext

XIAO-HUA ZHOU, PhD, is Professor in the Department of Biostatistics at the University of Washington and Director and Research Career Scientist at the Biostatistics Unit of the Veterans Affairs Puget Sound Health Care System. Dr. Zhou is Associate Editor of Statistics in Medicine and has published over 200 journal articles in his areas of research interest, which include statistical methods in diagnostic medicine, analysis of skewed data, causal inferences, and statistical methods for assessing predictive values of biomarkers. CHUAN ZHOU, PhD, is Research Associate Professor in the Department of Pediatrics at University of Washington. Dr. Zhou is also Senior Biostatistician at the Center for Child Health, Behavior and Development at Seattle Children's Research Institute where he conducts clinical and epidemiological research with pediatric population. His areas of research interest include clinical trials, health service research, diagnostics, missing data, and causal inference. DANPING LIU, PhD, is Investigator in the Division of Intramural Population Health Research at the Eunice Kennedy Shriver National Institute of Child Health and Human Development. He has authored numerous research articles in his areas of research interest, which include medical diagnostic testing and ROC curve, missing data methodologies, longitudinal data analysis, and non- and-semi-parametric inferences. XIAOBO DING, PhD, is Assistant Professor in the Academy of Mathematics and Systems Science at the Chinese Academy of Sciences. His areas of research interest include dimension reduction, variable selection, missing data, confidence bands, and goodness of fit tests.

Klappentext
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.

Zusammenfassung
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.

Inhalt
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