Non-Fiction Books:

Applied Missing Data Analysis in the Health Sciences

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Description

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.

Author Biography:

Xiao-Hua Zhou, PhD, is Professor in the Departmentof Biostatistics at the University of Washington and Director andResearch Career Scientist at the Biostatistics Unit of the VeteransAffairs Puget Sound Health Care System. Dr. Zhou is AssociateEditor of Statistics in Medicine and has published over 200journal articles in his areas of research interest, which includestatistical methods in diagnostic medicine, analysis of skeweddata, causal inferences, and statistical methods for assessingpredictive values of biomarkers. Chuan Zhou, PhD, is Research Associate Professorof Biostatistics in the Department of Pediatrics at University ofWashington. He has coauthored numerous journal articles in hisresearch areas of interest, which include clinical trials, healthservice research, diagnostics, missing data, and causalinference. Danping Liu, PhD, is Investigator in the Divisionof Intramural Population Health Research at the Eunice KennedyShriver National Institute of Child Health and HumanDevelopment. He has authored numerous research articles in hisresearch areas of interest, which include medical diagnostictesting and ROC curve, missing data methodologies, longitudinaldata analysis, and non- and-semi-parametric inferences. Xiaobo Ding, PhD, is AssistantProfessor in the Academy of Mathematics and Systems Science at theChinese Academy of Sciences. His research interests includedimension reduction, variable selection, missing data, confidencebands, and goodness of fit tests.
Release date Australia
July 18th, 2014
Audiences
  • Postgraduate, Research & Scholarly
  • Professional & Vocational
  • Undergraduate
Illustrations
Charts: 20 B&W, 0 Color; Drawings: 10 B&W, 0 Color; Graphs: 45 B&W, 0 Color
Pages
256
Dimensions
163x244x20
ISBN-13
9780470523810
Product ID
21558197

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