Applied Missing Data Analysis in Health Sciences

This book provides a modern, hands-on guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics. It acknowledges the limitations of established techniques and provides concrete applications of newly developed methods. It covers traditional techniques for missing data inference-including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods-and applies the methodology to rapidly developing areas of research. The book is ideal for courses on biostatistics at the upper-undergraduate and graduate levels and for health science researchers and applied statisticians.

About the Authors

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 of Biostatistics in the Department of Pediatrics at University of Washington. He has coauthored numerous journal articles in his research areas of interest, which 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 research areas of 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 research interests include dimension reduction, variable selection, missing data, confidence bands, and goodness of fit tests.