Applied missing data analysis in the health sciences /

Applied missing data analysis in the health sciences /

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Bibliographic Details
Author / Creator:	Zhou, Xiao-Hua, author.
Imprint:	Hoboken, New Jersey : John Wiley & Sons, [2014]
Description:	1 online resource
Language:	English
Series:	Statistics in practice.
Subject:	Medical statistics. Medicine -- Research -- Statistical methods. Data Interpretation, Statistical Biomedical Research -- methods Models, Statistical Research Design Medical statistics Medicine -- Research -- Statistical methods Medical statistics Medical statistics.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/13617142

Hidden Bibliographic Details
Other authors / contributors:	Zhou, Chuan, 1972- author. Liu, Danping, 1981- author. Ding, Xiaobo, author.
ISBN:	9781118573648 1118573641 9781118573631 1118573633 9781118573716 1118573714 0470523816 9780470523810 9780470523810
Digital file characteristics:	data file
Notes:	Includes bibliographical references and index. Print version record and CIP data provided by publisher.
Summary:	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 modern 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 methods and the field of diagnostic medicine.
Other form:	Print version: Zhou, Xiao-Hua. Applied missing data analysis in the health sciences. Hoboken, New Jersey : John Wiley & Sons, Inc., [2014] 9780470523810
Publisher's no.:	EB00063906 Recorded Books

Table of Contents:

Missing data concepts and motivating examples
Overview of methods for dealing with missing data
Design considerations in the presence of missing data
Crosssectional data methods
Longitudinal data methods
Survival analysis under ignorable missingness
Nonignorable missingness.