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Applied missing data analysis in health sciences / Xiao-Hua Zhou...[et al.].

By: Contributor(s): Series: Statistics in practicePublication details: New Jersey : John Wiley, 2014.Description: xviii, 230 p. ; illISBN:
  • 9780470523810
Subject(s): DDC classification:
  • 000SB:610.724 23 Z63
Contents:
1. Missing data concepts and motivating examples -- 2.Overview of methods for dealing with missing data -- 3. Design considerations in the presence of missing data -- 4. Crosssectional data methods -- 5. Longitudinal data methods -- 6. Survival analysis under ignorable missingness -- 7. Nonignorable missingness-- 8. Analysis of randomized clinical trials with noncompliance-- Bibliography.
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.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SB:610.724 Z63 (Browse shelf(Opens below)) Available 135809
Total holds: 0

Includes bibliographical references.

1. Missing data concepts and motivating examples --
2.Overview of methods for dealing with missing data --
3. Design considerations in the presence of missing data --
4. Crosssectional data methods --
5. Longitudinal data methods --
6. Survival analysis under ignorable missingness --
7. Nonignorable missingness--
8. Analysis of randomized clinical trials with noncompliance--
Bibliography.

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.

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