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E-grāmata: Applied Missing Data Analysis in the Health Sciences

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(Eunice Kennedy Shriver National Institute of Child Health and Human Development), (University of Washington), (University of Washington), (Chinese Academy of Sciences)
  • Formāts: PDF+DRM
  • Sērija : Statistics in Practice
  • Izdošanas datums: 15-May-2014
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118573631
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Applied Missing Data Analysis in the Health SciencesPalielināt
  • Formāts: PDF+DRM
  • Sērija : Statistics in Practice
  • Izdošanas datums: 15-May-2014
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118573631
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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.

Recenzijas

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)

 

List Of Figures
xv
List Of Tables
xvii
Preface xix
1 Missing Data Concepts And Motivating Examples
1(14)
1.1 Overview of the Missing Data Problem
1(1)
1.2 Patterns and Mechanisms of Missing Data
2(5)
1.2.1 Missing Data Patterns
3(2)
1.2.2 Missing Data Mechanisms
5(2)
1.3 Data Examples
7(8)
1.3.1 Improving Mood and Promoting Access to Collaborative Treatment (IMPACT) Study
9(1)
1.3.2 National Alzheimer's Coordinating Center Minimum Data Set
10(1)
1.3.3 National Alzheimer's Coordinating Center Uniform Data Set
11(1)
1.3.4 The Pathways Study
12(1)
1.3.5 Randomized Trial on Vitamin A Supplement
12(1)
1.3.6 Randomized Trial on Effectiveness of Flu Shot
12(3)
2 Overview Of Methods For Dealing With Missing Data
15(10)
2.1 Methods That Remove Observations
15(2)
2.1.1 Complete-Case Methods
16(1)
2.1.2 Weighted Complete-Case Methods
16(1)
2.1.3 Removing Variables with Large Amounts of Missing Values
17(1)
2.2 Methods That Utilize All Available Data
17(2)
2.2.1 Maximum Likelihood
18(1)
2.3 Methods That Impute Missing Values
19(5)
2.3.1 Single Imputation Methods
19(2)
2.3.2 Multiple Imputation
21(3)
2.4 Bayesian Methods
24(1)
3 Design Considerations In The Presence Of Missing Data
25(6)
3.1 Design Factors Related to Missing Data
25(2)
3.2 Strategies for Limiting Missing Data in the Design of Clinical Trials
27(1)
3.3 Strategies for Limiting Missing Data in the Conduct of Clinical Trials
28(1)
3.4 Minimize the Impact of Missing Data
29(2)
4 Cross-Sectional Data Methods
31(38)
4.1 Overview of General Methods
31(1)
4.2 Data Examples
31(1)
4.2.1 Simulation Study
32(1)
4.2.2 NHANES Example
32(1)
4.3 Maximum Likelihood Approach
32(8)
4.3.1 EM Algorithm for Linear Regression with a Missing Continuous Covariate
34(2)
4.3.2 EM Algorithm for Linear Regression with Missing Discrete Covariate
36(2)
4.3.3 EM Algorithm for Logistic Regression with Missing Binary Outcome
38(1)
4.3.4 Simulation Study
38(1)
4.3.5 IMPACT Study
39(1)
4.3.6 NACC Study
40(1)
4.4 Bayesian Methods
40(7)
4.4.1 Theory
40(1)
4.4.2 Joint Model and Ignorable Missingness
41(2)
4.4.3 Bayesian Computation for Missing Data
43(1)
4.4.4 Simulation Example
44(1)
4.4.5 IMPACT Study
45(1)
4.4.6 NHANES Example
45(2)
4.5 Multiple Imputation
47(7)
4.5.1 Theory
47(2)
4.5.2 Some General Guidelines on Imputation Models and Analysis Models
49(1)
4.5.3 Theoretical Justification for the MI Method
50(1)
4.5.4 MI When θ Is k-Dimensional
51(2)
4.5.5 Simulated Example
53(1)
4.5.6 IMPACT Study
53(1)
4.6 Imputing Estimating Equations
54(1)
4.7 Inverse Probability Weighting
55(1)
4.7.1 Theory
55(1)
4.7.2 Simulated Example
56(1)
4.8 Doubly Robust Estimators
56(4)
4.8.1 Theory
56(1)
4.8.2 Variance Estimation
57(2)
4.8.3 NACC Study
59(1)
4.9 Code Used in This
Chapter
60(9)
4.9.1 Code Used in Section 4.3.4
60(2)
4.9.2 Code Used in Section 4.3.5
62(1)
4.9.3 Code Used in Section 4.4.4
63(1)
4.9.4 Code Used in Section 4.4.5
64(1)
4.9.5 Code Used in Section 4.4.6
65(1)
4.9.6 Code Used in Section 4.5.5
66(1)
4.9.7 Code Used in Section 4.5.6
66(1)
4.9.8 Code Used in Section 4.7.2
67(2)
5 Longitudinal Data Methods
69(52)
5.1 Overview
69(1)
5.2 Examples
70(3)
5.2.1 IMPACT Study
70(1)
5.2.2 NACC UDS Data
71(2)
5.3 Longitudinal Regression Models for Complete Data
73(9)
5.3.1 Linear Mixed Models for Continuous Longitudinal Data
73(4)
5.3.2 Generalized Estimating Equations
77(2)
5.3.3 Generalized Linear Mixed Models
79(2)
5.3.4 Time-Dependent Covariates
81(1)
5.4 Missing Data Settings and Simple Methods
82(1)
5.4.1 Setup
82(1)
5.4.2 Simple Methods
82(1)
5.5 Likelihood Approach
83(3)
5.5.1 Example: IMPACT Study
84(2)
5.6 Inverse Probability Weighted GEE with MAR Dropout
86(5)
5.6.1 Modeling the Selection Probability
86(1)
5.6.2 IPWGEE1 and IPWGEE2
87(2)
5.6.3 A Simulation Study
89(1)
5.6.4 Example: IMPACT Study
90(1)
5.7 Extension to Nonmonotone Missingness
91(1)
5.8 Multiple Imputation
91(8)
5.8.1 Joint Imputation Model
92(1)
5.8.2 Imputation by Chained Equations
93(1)
5.8.3 Example: NACC UDS Data
94(2)
5.8.4 Example: IMPACT Study
96(3)
5.9 Bayesian Inference
99(2)
5.10 Other Approaches
101(20)
5.10.1 Imputing Estimating Equations
101(2)
5.10.2 Doubly Robust Estimation
103(1)
5.10.3 Missing Outcome and Covariates
104(5)
Appendix 5.A Technical Details of the Approximation Methods for GLMM and Computer Code for the Examples
109(1)
5.A.1 PQL and MQL
109(1)
5.A.2 Laplace Approximation
110(1)
5.A.3 Gaussian Quadrature
111(1)
5.A.4 Code for This
Chapter
111(10)
6 Survival Analysis Under Ignorable Missingness
121(26)
6.1 Overview
121(1)
6.2 Introduction
122(3)
6.2.1 Review of the Cox Model with Completely Observed Covariates
122(2)
6.2.2 Missing Data
124(1)
6.3 Enhanced Complete-Case Analysis
125(2)
6.4 Weighted Methods
127(9)
6.4.1 Simple Weighted Estimations
127(4)
6.4.2 Augmented Weighted Estimation
131(3)
6.4.3 Reweighting Estimators
134(2)
6.5 Imputation Methods
136(3)
6.5.1 Imputation by Conditional Expectations
136(1)
6.5.2 Multiple Imputation
137(2)
6.6 Nonparametric Maximum Likelihood Estimation
139(1)
6.7 Transformation Model
140(2)
6.8 Data Example: Pathways Study
142(2)
6.9 Concluding Remarks
144(3)
7 Nonignorable Missingness
147(38)
7.1 Introduction
147(2)
7.2 Cross-Sectional Data: Selection Model
149(1)
7.2.1 Missing Outcome
149(1)
7.2.2 Missing Covariate
150(1)
7.3 Longitudinal Data with Dropout
150(11)
7.3.1 Notation
150(1)
7.3.2 Overview
151(1)
7.3.3 Pattern Mixture Model
151(4)
7.3.4 Selection Model
155(2)
7.3.5 Shared Random Effects Model
157(2)
7.3.6 Mixed Effects Hybrid Model
159(1)
7.3.7 Comparison of Likelihood-Based Methods
160(1)
7.4 Bayesian Analysis for Generalized Linear Models with Nonignorably Missing Covariates
161(4)
7.4.1 Generalized Linear Models
162(1)
7.4.2 Modeling the Covariate Distribution
163(1)
7.4.3 Modeling the Missing Data Mechanism
163(1)
7.4.4 Prior Distributions
164(1)
7.4.5 Posterior Computation
165(1)
7.5 Multiple Imputation
165(5)
7.5.1 Proper Multiple Imputation by Bayesian Analysis
166(1)
7.5.2 Approximate Bayesian Bootstrap Hot Deck Imputation
167(3)
7.6 Inverse Probability Weighted Methods
170(15)
7.6.1 Semiparametric Regression for Repeated Outcomes
170(4)
7.6.2 Estimation of Marginal Mean of an Outcome from Longitudinal Data
174(11)
8 Analysis Of Randomized Clinical Trials With Noncompliance
185(30)
8.1 Overview
185(1)
8.2 Examples
186(2)
8.2.1 Randomized Trial on Vitamin A Supplement
186(1)
8.2.2 Randomized Trial on Effectiveness of Flu Shot
187(1)
8.3 Some Common but Naive Methods
188(1)
8.4 Notations, Assumptions, and Causal Definitions
189(3)
8.5 Method of Instrumental Variables
192(2)
8.6 Moment-Based Method
194(3)
8.7 Maximum Likelihood and Bayesian Methods
197(5)
8.8 Noncompliance and Missing Outcome Data
202(9)
8.9 Analysis of the Two Examples
211(1)
8.9.1 Analysis of Vitamin A Supplement Example
211(1)
8.9.2 Flu Shot Example
212(1)
8.10 Other Methods for Dealing with Both Noncompliance and Missing Data
212(3)
Appendix 8.A Multivariate Delta Method
213(2)
Bibliography 215(10)
Index 225
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 Childrens 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.
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