Zhou / Lui / Ding Applied Missing Data Analysis in the Health Sciences

1. Auflage 2014
ISBN: 978-1-118-57363-1
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 256 Seiten, E-Book

Reihe: Statistics in Practice

A 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.

Autoren/Hrsg.

Zhou, Xiao-Hua

Zhou, Chuan

Lui, Danping

Ding, Xaiobo

Inhaltsverzeichnis

List of Figures xv
List of Tables xvii
Preface xix
Introduction xxi
1 Missing Data Concepts and Motivating Examples 1
1.1 Overview of Missing Data Problem 1
1.2 Mechanisms 3
1.3 Data examples 8
2 Overview of Methods for Dealing with Missing Data 19
2.1 Methods that remove observations 20
2.2 Methods that utilize all available data 21
2.3 Methods that impute missing values 22
3 Design Considerations in the Presence of Missing Data 31
3.1 Design factors related to missing data 32
3.2 Strategies for limiting missing data in the design of clinical trials 33
3.3 Strategies for limiting missing data in the conduct of clinical trials 34
3.4 Minimize the impact of missing data 35
3.5 Sample size and power consideration in the presence of missing data 36
4 Cross-sectional Data Methods 41
4.1 Overview of General Methods 41
4.2 Data Examples 42
4.3 Maximum Likelihood Approach 44
4.4 Bayesian Methods 61
4.5 Multiple Imputation 71
4.6 Inverse Probability Weighting 76
4.7 Weighted Estimating Equation Approaches 79
4.8 Doubly Robust Estimators 80
4.9 Additional Theories 83
5 Longitudinal Data Methods 97
5.1 Overview of Chapter 97
5.2 Examples 98
5.3 Longitudinal Regression Models for Complete Data 101
5.4 Missing Data Settings and Simple Methods 111
5.5 Likelihood Approach 112
5.6 Weighted GEE (WEE) with MAR Dropout 117
5.7 Extension to Nonmonotone Missingness 123
5.8 Multiple Imputation (MI) 125
5.9 Bayesian Inference 139
5.10 Other Approaches 141
5.11 Appendix: Technical Details 149
6 Survival Analysis under Ignorable Missingness 153
6.1 Overview of the chapter 153
6.2 Introductions 154
6.3 Enhanced complete-case analysis 157
6.4 Weighted methods 159
6.5 Imputation methods 168
6.6 Nonparametric maximum likelihood estimation 171
6.7 Transformation model 172
6.8 Pathways study 174
6.9 Concluding remarks 175
7 Nonignorable Missingness 177
7.1 Introduction 177
7.2 Cross-sectional data: selection model 179
7.3 Longitudinal data with dropout 180
7.4 Bayesian analysis for GLMs 191
7.5 Multiple imputation 195
7.6 Inverse probability weighted methods 199
8 Analysis of Randomized Clinical Trials with Non-Compliance 215
8.1 Overview of the chapter 215
8.2 Examples 217
8.3 Some Common but Naive Methods 218
8.4 Notations, Assumptions, and Causal Definitions 220
8.5 Method of Instrumental Variables 223
8.6 Another Moment-based Method 224
8.7 Maximum Likelihood and Bayesian Method 227
8.8 Noncompliance and Missing Some Outcome Data 232
8.9 Analysis of the Two Examples 241
8.10 Other Methods for Dealing with both Noncompliance and Missingdata 242
8.11 Appendix: Multivariate Delta Method 243

Über Autor(innen)

Xiao-Hua Zhou, PhD, is Professor in the Departmentof Biostatistics at the University of Washington and Director andResearch Career Scientist at the Biostatistics Unit of the VeteransAffairs Puget Sound Health Care System. Dr. Zhou is AssociateEditor of Statistics in Medicine and has published over 200journal articles in his areas of research interest, which includestatistical methods in diagnostic medicine, analysis of skeweddata, causal inferences, and statistical methods for assessingpredictive values of biomarkers.
Chuan Zhou, PhD, is Research Associate Professorof Biostatistics in the Department of Pediatrics at University ofWashington. He has coauthored numerous journal articles in hisresearch areas of interest, which include clinical trials, healthservice research, diagnostics, missing data, and causalinference.
Danping Liu, PhD, is Investigator in the Divisionof Intramural Population Health Research at the Eunice KennedyShriver National Institute of Child Health and HumanDevelopment. He has authored numerous research articles in hisresearch areas of interest, which include medical diagnostictesting and ROC curve, missing data methodologies, longitudinaldata analysis, and non- and-semi-parametric inferences.
Xiaobo Ding, PhD, is AssistantProfessor in the Academy of Mathematics and Systems Science at theChinese Academy of Sciences. His research interests includedimension reduction, variable selection, missing data, confidencebands, and goodness of fit tests.