Applied Missing Data Analysis in the Health Sciences

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.

“Overall the book is an excellent reference for biostatisticians who are interested in methodological approaches as well as for biostatisticians who prefer the applied side. Several useful examples from clinical trials and health research are carefully selected and analyzed to demonstrate the methods covered in the book. It is also a useful resource for postgraduate students researching missing-data methods and their application.” (Biometrical Journal, 1 June 2015)

• 1 Missing Data Concepts and Motivating Examples 12 Overview of Methods for Dealing with Missing Data 153 Design Considerations in the Presence Of Missing Data 254 Cross-sectional Data Methods 315 Longitudinal Data Methods 696 Survival Analysis Under Ignorable Missingness 1217 Nonignorable Missingness 1478 Analysis of Randomized Clinical Trials With Noncompliance 185Bibliography 215Index 225