Applied Missing Data Analysis in the Health Sciences

Xiao-Hua Zhou author Chuan Zhou author Danping Lui author Xaiobo Ding author

Format:Hardback

Publisher:John Wiley & Sons Inc

Published:18th Jul '14

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Applied Missing Data Analysis in the Health Sciences cover

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.

“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)

 

ISBN: 9780470523810

Dimensions: 244mm x 163mm x 20mm

Weight: 481g

256 pages