Multiple Imputation in Practice: With Examples Using IVEware provides practical guidance on multiple imputation analysis, from simple to complex problems using real and simulated data sets. Data sets from cross-sectional, retrospective, prospective and longitudinal studies, randomized clinical trials, complex sample surveys are used to illustrate both simple, and complex analyses. Version 0.3 of IVEware, the software developed by the University of Michigan, is used to illustrate analyses. IVEware can multiply impute missing values, analyze multiply imputed data sets, incorporate complex sample design features, and be used for other statistical analyses framed as missing data problems. IVEware can be used under Windows, Linux, and Mac, and with software packages like SAS, SPSS, Stata, and R, or as a stand-alone tool. This book will be helpful to researchers looking for guidance on the use of multiple imputation to address missing data problems, along with examples of correct analysis techniques.-- Provided by Publisher

lgli/CRC - Multiple Imputation in Practice with Examples using IVEware 2018.pdf

lgrsnf/CRC - Multiple Imputation in Practice with Examples using IVEware 2018.pdf

zlib/Mathematics/Trivellore Raghunathan, Patricia A. Berglund, Peter W. Solenberger/Multiple Imputation in Practice with Examples using IVEware_3588706.pdf

Raghunathan, Trivellore, Berglund, Patricia A., Solenberger, Peter W.

Trivellore Raghunathan, Peter W. Solenberger, Patricia A. Berglund

Chapman and Hall/CRC

CRC Press LLC

United States, United States of America

Boca Raton, Florida, 2018

1, 2018

0

lg2261479

{"isbns":["1498770169","9781498770163"],"last_page":255,"publisher":"CRC Press"}

Contents......Page 3
Preface......Page 8
Denition of a Missing Value......Page 12
Patterns of Missing Data......Page 13
Missing Data Mechanisms......Page 14
What is Imputation?......Page 16
General Framework for Imputation......Page 20
Sequential Regression Multivariate Imputation (SRMI)......Page 21
How Many Iterations?......Page 23
A Technical Issue......Page 24
Three-variable Example......Page 25
Complex Sample Surveys......Page 30
Imputation Diagnostics......Page 31
Should We Impute or Not?......Page 33
Is Imputation Making Up Data?......Page 34
Multiple Imputation Analysis......Page 35
Multiple Imputation Theory......Page 37
Number of Imputations......Page 40
Additional Reading......Page 41
Introduction......Page 44
Imputation Task......Page 46
Descriptive Analysis......Page 49
Practical Considerations......Page 52
Exercises......Page 53
Introduction......Page 56
Complete Data Inference......Page 57
Comparing Blocks of Variables......Page 59
Model Diagnostics......Page 60
Multiple Imputation Analysis......Page 61
Example......Page 63
Additional Reading......Page 69
Exercises......Page 70
Introduction......Page 73
Multiple Imputation Analysis......Page 74
Additional Reading......Page 83
Exercises......Page 84
Contingency Table Analysis......Page 88
Log-linear Models......Page 89
Three-way Contingency Table......Page 91
Two-way Contingency Table......Page 92
Three-way Contingency Table......Page 97
Additional Reading......Page 104
Exercises......Page 105
Introduction......Page 108
Multiple Imputation Analysis......Page 109
Additional Reading......Page 115
Exercises......Page 116
Introduction......Page 119
Example......Page 121
Multiple Imputation Analysis......Page 123
Exercises......Page 125
Introduction......Page 128
Example 1: Binary Outcome......Page 130
Example 2: Continuous Outcome......Page 133
Example 3: A Case Study......Page 138
Discussion......Page 151
Additional Reading......Page 152
Exercises......Page 153
Introduction......Page 156
Example......Page 158
Exercises......Page 168
Introduction......Page 170
Pattern-Mixture Model......Page 171
Examples......Page 173
Additional Reading......Page 183
Exercises......Page 184
Imputing Scores......Page 187
Imputation and Analysis Models......Page 188
Running Simulations Using......Page 190
Congeniality and Multiple Imputations......Page 195
Combining Bayesian Inferences......Page 198
Imputing Interactions......Page 204
Final Thoughts......Page 209
Additional Reading......Page 210
Exercises......Page 211
A.1 St. Louis Risk Research Project......Page 213
A.2 Primary Biliary Cirrhosis Data Set......Page 214
A.3 Opioid Detoxication Data Set......Page 217
A.5 National Comorbidity Survey Replication (NCS-R)......Page 218
A.6 National Health and Nutrition Examination Survey, -2012 (NHANES 2011-2012)......Page 220
A.7 Health and Retirement Study, 2012 (HRS 2012)......Page 225
A.8 Case Control Data for Omega-3 Fatty Acids and Primary Cardiac Arrest......Page 226
A.9 National Merit Twin Study......Page 229
A.11 Outline of Analysis Examples and Data Sets......Page 230
IVEware......Page 233
Biblio......Page 239
Index......Page 252

This book provides practical guidance on multiple imputation analysis, from simple to complex problems using real and simulated data sets. Data sets from cross-sectional, retrospective, prospective and longitudinal studies, randomized clinical trials, complex sample surveys are used to illustrate both simple, and complex analyses.

2018-09-05