{"product_id":"applied-missing-data-analysis-in-the-health-sciences-isbn-9780470523810","title":"Applied Missing Data Analysis in the Health Sciences","description":"\u003cb\u003eApplied Missing Data Analysis in the Health Sciences\u003c\/b\u003e \u003cp\u003e\u003cb\u003eA modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWith an emphasis on hands-on applications, \u003ci\u003eApplied Missing Data Analysis in the Health Sciences \u003c\/i\u003eoutlines 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.\u003c\/p\u003e \u003cp\u003eOrganized 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, \u003ci\u003eApplied Missing Data Analysis in the Health Sciences \u003c\/i\u003efeatures:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eMultiple data sets that can be replicated using SAS®, Stata®, R, and WinBUGS software packages\u003c\/li\u003e \u003cli\u003eNumerous examples of case studies to illustrate real-world scenarios and demonstrate applications of discussed methodologies\u003c\/li\u003e \u003cli\u003eDetailed appendices to guide readers through the use of the presented data in various software environments\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eApplied Missing Data Analysis in the Health Sciences \u003c\/i\u003eis an excellent textbook for upper-undergraduate and graduate-level biostatistics courses as well as an ideal resource for health science researchers and applied statisticians.\u003c\/p\u003e \u003cp\u003e1 Missing Data Concepts and Motivating Examples 1\u003c\/p\u003e \u003cp\u003e2 Overview of Methods for Dealing with Missing Data 15\u003c\/p\u003e \u003cp\u003e3 Design Considerations in the Presence Of Missing Data 25\u003c\/p\u003e \u003cp\u003e4 Cross-sectional Data Methods 31\u003c\/p\u003e \u003cp\u003e5 Longitudinal Data Methods 69\u003c\/p\u003e \u003cp\u003e6 Survival Analysis Under Ignorable Missingness 121\u003c\/p\u003e \u003cp\u003e7 Nonignorable Missingness 147\u003c\/p\u003e \u003cp\u003e8 Analysis of Randomized Clinical Trials With Noncompliance 185\u003c\/p\u003e \u003cp\u003eBibliography 215\u003c\/p\u003e \u003cp\u003eIndex 225\u003c\/p\u003e \u003cp\u003e“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.” \u003ci\u003e(\u003c\/i\u003e\u003ci\u003eBiometrical Journal\u003c\/i\u003e, 1 June 2015)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eXIAO-HUA ZHOU, PhD, \u003c\/b\u003eis Professor in the Department of Biostatistics at the University of Washington and Director and Research Career Scientist at the Biostatistics Unit of the Veterans Affairs Puget Sound Health Care System. Dr. Zhou is Associate Editor of \u003ci\u003eStatistics in Medicine \u003c\/i\u003eand has published over 200 journal articles in his areas of research interest, which include statistical methods in diagnostic medicine, analysis of skewed data, causal inferences, and statistical methods for assessing predictive values of biomarkers. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eCHUAN ZHOU, PhD, \u003c\/b\u003eis Research Associate Professor in the Department of Pediatrics at University of Washington. Dr. Zhou is also Senior Biostatistician at the Center for Child Health, Behavior and Development at Seattle Children’s Research Institute where he conducts clinical and epidemiological research with pediatric population. His areas of research interest include clinical trials, health service research, diagnostics, missing data, and causal inference. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDANPING LIU, PhD, \u003c\/b\u003eis Investigator in the Division of Intramural Population Health Research at the Eunice Kennedy Shriver National Institute of Child Health and Human Development. He has authored numerous research articles in his areas of research interest, which include medical diagnostic testing and ROC curve, missing data methodologies, longitudinal data analysis, and non- and-semi-parametric inferences. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eXIAOBO DING, PhD, \u003c\/b\u003eis Assistant Professor in the Academy of Mathematics and Systems Science at the Chinese Academy of Sciences. His areas of research interest include dimension reduction, variable selection, missing data, confidence bands, and goodness of fit tests.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWith an emphasis on hands-on applications, \u003ci\u003eApplied Missing Data Analysis in the Health Sciences \u003c\/i\u003eoutlines 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.\u003c\/p\u003e \u003cp\u003eOrganized 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, \u003ci\u003eApplied Missing Data Analysis in the Health Sciences \u003c\/i\u003efeatures:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eMultiple data sets that can be replicated using SAS®, Stata®, R, and WinBUGS software packages\u003c\/li\u003e \u003cli\u003eNumerous examples of case studies to illustrate real-world scenarios and demonstrate applications of discussed methodologies\u003c\/li\u003e \u003cli\u003eDetailed appendices to guide readers through the use of the presented data in various software environments\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eApplied Missing Data Analysis in the Health Sciences \u003c\/i\u003eis an excellent textbook for upper-undergraduate and graduate-level biostatistics courses as well as an ideal resource for health science researchers and applied statisticians.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988752187621,"sku":"NP9780470523810","price":100.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470523810.jpg?v=1761781451","url":"https:\/\/k12savings.com\/es\/products\/applied-missing-data-analysis-in-the-health-sciences-isbn-9780470523810","provider":"K12savings","version":"1.0","type":"link"}