Applied Missing Data Analysis in the Health Sciences (eBook)
256 Seiten
John Wiley & Sons (Verlag)
978-1-118-57364-8 (ISBN)
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 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.
Organized 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, Applied Missing Data Analysis in the Health Sciences features:
* Multiple data sets that can be replicated using SAS¯®, Stata¯®, R, and WinBUGS software packages
* Numerous examples of case studies 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.
XIAO-HUA ZHOU, PhD, is 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 Statistics in Medicine and 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. CHUAN ZHOU, PhD, is 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. DANPING LIU, PhD, is 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. XIAOBO DING, PhD, is 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.
1 Missing Data Concepts and Motivating Examples 1
2 Overview of Methods for Dealing with Missing Data 15
3 Design Considerations in the Presence Of Missing Data 25
4 Cross-sectional Data Methods 31
5 Longitudinal Data Methods 69
6 Survival Analysis Under Ignorable Missingness 121
7 Nonignorable Missingness 147
8 Analysis of Randomized Clinical Trials With Noncompliance 185
Bibliography 215
Index 225
"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)
Erscheint lt. Verlag | 19.5.2014 |
---|---|
Reihe/Serie | Statistics in Practice | Statistics in Practice |
Sprache | englisch |
Themenwelt | Mathematik / Informatik ► Mathematik ► Statistik |
Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
Studium ► Querschnittsbereiche ► Epidemiologie / Med. Biometrie | |
Technik | |
Schlagworte | Bayesian analysis • Bayes-Verfahren • Biostatistics • Biostatistik • Epidemiologie u. Biostatistik • Epidemiology & Biostatistics • Gesundheits- u. Sozialwesen • Health & Social Care • Statistics • Statistik |
ISBN-10 | 1-118-57364-1 / 1118573641 |
ISBN-13 | 978-1-118-57364-8 / 9781118573648 |
Haben Sie eine Frage zum Produkt? |
Größe: 2,8 MB
Kopierschutz: Adobe-DRM
Adobe-DRM ist ein Kopierschutz, der das eBook vor Mißbrauch schützen soll. Dabei wird das eBook bereits beim Download auf Ihre persönliche Adobe-ID autorisiert. Lesen können Sie das eBook dann nur auf den Geräten, welche ebenfalls auf Ihre Adobe-ID registriert sind.
Details zum Adobe-DRM
Dateiformat: EPUB (Electronic Publication)
EPUB ist ein offener Standard für eBooks und eignet sich besonders zur Darstellung von Belletristik und Sachbüchern. Der Fließtext wird dynamisch an die Display- und Schriftgröße angepasst. Auch für mobile Lesegeräte ist EPUB daher gut geeignet.
Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen eine
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen eine
Geräteliste und zusätzliche Hinweise
Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.
aus dem Bereich