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„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)
Applied Missing Data Analysis in the Health Sciences
von Xiao-Hua Zhou, Chuan Zhou, Danping Lui und Xaiobo DingA modern and practical guide to the essential concepts andideas for analyzing data with missing observations in the field ofbiostatistics
With an emphasis on hands-on applications, Applied MissingData Analysis in the Health Sciences outlines the variousmodern statistical methods for the analysis of missing data. Theauthors acknowledge the limitations of established techniques andprovide newly-developed methods with concrete applications in areassuch as causal inference methods and the field of diagnosticmedicine.
Organized by types of data, chapter coverage begins with anoverall introduction to the existence and limitations of missingdata and continues into traditional techniques for missing datainference, including likelihood-based, weighted GEE, multipleimputation, and Bayesian methods. The book's subsequentlycovers cross-sectional, longitudinal, hierarchical, survival data. In addition, Applied Missing Data Analysis in the HealthSciences 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 biostatisticsto illustrate real-world scenarios and demonstrate applications ofdiscussed methodologies
* Detailed appendices to guide readers through the use of thepresented data in various software environments
Applied Missing Data Analysis in the Health Sciences isan excellent textbook for upper-undergraduate and graduate-levelbiostatistics courses as well as an ideal resource for healthscience researchers and applied statisticians.
With an emphasis on hands-on applications, Applied MissingData Analysis in the Health Sciences outlines the variousmodern statistical methods for the analysis of missing data. Theauthors acknowledge the limitations of established techniques andprovide newly-developed methods with concrete applications in areassuch as causal inference methods and the field of diagnosticmedicine.
Organized by types of data, chapter coverage begins with anoverall introduction to the existence and limitations of missingdata and continues into traditional techniques for missing datainference, including likelihood-based, weighted GEE, multipleimputation, and Bayesian methods. The book's subsequentlycovers cross-sectional, longitudinal, hierarchical, survival data. In addition, Applied Missing Data Analysis in the HealthSciences 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 biostatisticsto illustrate real-world scenarios and demonstrate applications ofdiscussed methodologies
* Detailed appendices to guide readers through the use of thepresented data in various software environments
Applied Missing Data Analysis in the Health Sciences isan excellent textbook for upper-undergraduate and graduate-levelbiostatistics courses as well as an ideal resource for healthscience researchers and applied statisticians.
