Applied Missing Data Analysis in the Health Sciences

About this book:
Methods for analyzing missing data are becoming increasingly important when designing and analyzing studies in the health sciences. This book is an excellent addition to this important area of research. It covers both methodological approaches and applications of the methods under a variety of assumptions related to missing-data processes. The unique feature about the book is that it covers the methodological development in great depth for those who are methods-inclined, but it also includes real dataset examples for those who are interested in applications. Simulation studies are used to strengthen the methodological arguments. The authors also include software code in R and Stata, which the reader can adapt to his/her situation. It would have made an excellent teaching book for an advanced course on missing-data methods if the authors had included sample questions and answers. The book comprises eight chapters, the first six of which are based on the assumption of ignorable missingness. The last two chapters cover more advanced material—that is, nonignorable missingness and the analysis of randomized clinical trials with noncompliance.