Applied missing data analysis in the health sciences

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 modern statistical methods for the analysis...

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Bibliographic Details
Other Authors: Zhou, Xiao-hua Author (author), Ding, Xiaobo Author (contributor), Liu, Danping Contributor, Liu, Danping Author, Zhou, Chuan Contributor, Zhou, Chuan Author
Format: eBook
Language:Inglés
Published: [Place of publication not identified] John Wiley & Sons Inc 2014
Hoboken New Jersey 2014
Edition:1st edition
Series:Statistics in Practice. Applied missing data analysis in the health sciences
Subjects:
Statistics as Topic.
Decision Support Techniques.
Investigative Techniques.
Research.
Models, Theoretical.
Science.
Epidemiologic Methods.
Health Care Evaluation Mechanisms.
Medical Informatics Applications.
Public Health.
Natural Science Disciplines.
Quality of Health Care.
Medical Informatics.
Environment and Public Health.
Information Science.
Health Care Quality, Access, and Evaluation.
Delivery of Health Care.
Data Interpretation, Statistical.
Research Design.
Models, Statistical.
Biomedical Research.
Methods.
Medicine
Health & Biological Sciences
Medical Education
Medical Research
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629431506719
Description
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. Organized by types of data, chapter coverage begins with an overall introduction to the existence and limitations of missing data and continues into traditional techniques for missing data inference, including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods. The book's 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 the SAS, Stata, R, and WinBUGS software packages Numerous examples of case studies in the field of biostatistics 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.
Item Description:Bibliographic Level Mode of Issuance: Monograph
Physical Description:1 online resource (256 pages)

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