Flexible imputation of missing data:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Florida
CRC Press
2018
|
| Ausgabe: | Second edition |
| Schriftenreihe: | Chapman & Hall/CRC interdisciplinary statistics series
A Chapman & Hall book |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxvii, 415 Seiten Diagramme (teilweise farbig) |
| ISBN: | 9781138588318 |
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| 245 | 1 | 0 | |a Flexible imputation of missing data |c Stef van Buuren |
| 250 | |a Second edition | ||
| 264 | 1 | |a Boca Raton, Florida |b CRC Press |c 2018 | |
| 264 | 4 | |c © 2018 | |
| 300 | |a xxvii, 415 Seiten |b Diagramme (teilweise farbig) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Chapman & Hall/CRC interdisciplinary statistics series | |
| 490 | 0 | |a A Chapman & Hall book | |
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| 653 | 0 | |a Multivariate analysis | |
| 653 | 0 | |a Multiple imputation (Statistics) | |
| 653 | 0 | |a Missing observations (Statistics) | |
| 689 | 0 | 0 | |a Multivariate Analyse |0 (DE-588)4040708-1 |D s |
| 689 | 0 | 1 | |a R |g Programm |0 (DE-588)4705956-4 |D s |
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| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, web pdf |z 978-0-429-96035-2 |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, epub |z 978-0-429-96034-5 |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, mobi / kindle |z 978-0-429-96033-8 |
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Datensatz im Suchindex
| _version_ | 1804178803396706304 |
|---|---|
| adam_text | Contents
Foreword by Donald B. Rubin xv
Preface to second edition xvii
Preface to first edition xxi
About the author xxiii
List of symbols xxv
List of algorithms xxvii
I Basics 1
1 Introduction 3
1.1 The problem of missing data ................................ 3
1.1.1 Current practice..................................... 3
1.1.2 Changing perspective on missing data................. 6
1.2 Concepts of MCAR, MAR and MNAR ......................... 8
1.3 Ad-hoc solutions............................................ 9
1.3.1 List wise deletion................................... 9
1.3.2 Pairwise deletion................................... 11
1.3.3 Mean imputation..................................... 12
1.3.4 Regression imputation............................... 13
1.3.5 Stochastic regression imputation.................... 14
1.3.6 LOCF and BOCF....................................... 16
1.3.7 Indicator method.................................... 17
1.3.8 Summary............................................. 18
1.4 Multiple imputation in a nutshell ......................... 19
1.4.1 Procedure........................................... 19
1.4.2 Reasons to use multiple imputation ................. 20
1.4.3 Example of multiple imputation ..................... 21
1.5 Goal of the book........................................... 23
1.6 What the book does not cover .............................. 23
1.6.1 Prevention ......................................... 24
1.6.2 Weighting procedures................................ 24
1.6.3 Likelihood-based approaches ........................ 25
vii
viii Contents
1.7 Structure of the book ................................... 26
1.8 Exercises ................................................. 26
2 Multiple imputation 29
2.1 Historic overview ......................................... 29
2.1.1 Imputation........................................... 29
2.1.2 Multiple imputation.................................. 30
2.1.3 The expanding literature on multiple imputation ... 32
2.2 Concepts in incomplete data ............................. 33
2.2.1 Incomplete-data perspective.......................... 33
2.2.2 Causes of missing data............................... 33
2.2.3 Notation............................................. 35
2.2.4 MCAR, MAR and MNAR again............................. 36
2.2.5 Ignorable and nonignorable*.......................... 38
2.2.6 Implications of ignorability......................... 39
2.3 Why and when multiple imputation works .................. 41
2.3.1 Goal of multiple imputation.......................... 41
2.3.2 Three sources of variation*.......................... 41
2.3.3 Proper imputation.................................... 44
2.3.4 Scope of the imputation model........................ 46
2.3.5 Variance ratios*..................................... 46
2.3.6 Degrees of freedom*.................................. 47
2.3.7 Numerical example.................................... 48
2.4 Statistical intervals and tests............................ 49
2.4.1 Scalar or multi-parameter inference?................. 49
2.4.2 Scalar inference..................................... 60
2.4.3 Numerical example.................................... 60
2.5 How to evaluate imputation methods ........................ 61
2.5.1 Simulation designs and performance measures.......... 51
2.5.2 Evaluation criteria.................................. 52
2.5.3 Example.............................................. 53
2.6 Imputation is not prediction .............................. 55
2.7 When not to use multiple imputation ....................... 57
2.8 How many imputations? ..................................... 58
2.9 Exercises ................................................. 01
3 Univariate missing data 63
3.1 How to generate multiple imputations....................... 63
3.1.1 Predict method....................................... 65
3.1.2 Predict + noise method............................... 65
3.1.3 Predict + noise + parameter uncertainty.............. 65
3.1.4 A second predictor................................... 66
3.1.5 Drawing from the observed data....................... 66
3.1.6 Conclusion........................................... 66
3.2 Imputation under the normal linear normal.................. 67
Contents
IX
3.2.1 Overview.............................................. 67
3.2.2 Algorithms* .......................................... 67
3.2.3 Performance .......................................... 69
3.2.4 Generating MAR missing data........................... 70
3.2.5 MAR missing data generation in multivariate data . . 72
3.2.6 Conclusion............................................ 73
3.3 Imputation under non-normal distributions.................... 74
3.3.1 Overview.............................................. 74
3.3.2 Imputation from the ¿-distribution.................... 75
3.4 Predictive mean matching ............................ 77
3.4.1 Overview.............................................. 77
3.4.2 Computational details*................................ 79
3.4.3 Number of donors...................................... 81
3.4.4 Pitfalls.............................................. 82
3.4.5 Conclusion............................................ 84
3.5 Classification and regression trees ......................... 84
3.5.1 Overview.............................................. 84
3.6 Categorical data............................................. 87
3.6.1 Generalized linear model.............................. 87
3.6.2 Perfect prediction*................................... 89
3.6.3 Evaluation ........................................... 90
3.7 Other data types............................................. 91
3.7.1 Count data............................................ 91
3.7.2 Semi-continuous data.................................. 92
3.7.3 Censored, truncated and rounded data.................. 93
3.8 Nonignorable missing data ............................ 96
3.8.1 Overview.............................................. 96
3.8.2 Selection model....................................... 97
3.8.3 Pattern-mixture model................................. 98
3.8.4 Converting selection and pattern-mixture models ... 99
3.8.5 Sensitivity analysis................................. 100
3.8.6 Role of sensitivity analysis......................... 101
3.8.7 Recent developments.................................. 102
3.9 Exercises ................................................. 102
4 Multivariate missing data 105
4.1 Missing data pattern ....................................... 105
4.1.1 Overview............................................. 105
4.1.2 Summary statistics................................... 107
4.1.3 Influx and outflux................................... 109
4.2 Issues in multivariate imputation........................... Ill
4.3 Monotone data imputation ........................... 112
4.3.1 Overview............................................. 112
4.3.2 Algorithm............................................ 113
4.4 Joint modeling.............................................. 115
X
Contents
4.4.1 Overview.......................................... 115
4.4.2 Continuous data................................... 115
4.4.3 Categorical data.................................. 117
4.5 Fully conditional specification ......................... 119
4.5.1 Overview.......................................... 119
4.5.2 The MICE algorithm.................................. 120
4.5.3 Compatibility*...................................... 122
4.5.4 Congeniality or compatibility? ..................... 124
4.5.5 Model-based and data-based imputation.............. 125
4.5.6 Number of iterations................................ 126
4.5.7 Example of slow convergence......................... 126
4.5.8 Performance ........................................ 129
4.6 FCS and JM ................................................ 130
4.6.1 Relations between FCS and JM........................ 130
4.6.2 Comparisons......................................... 130
4.6.3 Illustration........................................ 131
4.7 MICE extensions.......................................... 135
4.7.1 Skipping imputations and overimputation............. 135
4.7.2 Blocks of variables, hybrid imputation.............. 135
4.7.3 Blocks of units, monotone blocks.................... 136
4.7.4 Tile imputation..................................... 136
4.8 Conclusion ................................................ 137
4.9 Exercises ................................................. 137
5 Analysis of imputed data 139
5.1 Workflow................................................... 139
5.1.1 Recommended workflows .............................. 140
5.1.2 Not recommended workflow: Averaging the data . . . 142
5.1.3 Not recommended workflow: Stack imputed data . . . 144
5.1.4 Repeated analyses................................... 144
5.2 Parameter pooling.......................................... 145
5.2.1 Scalar inference of normal quantities............... 145
5.2.2 Scalar inference of non-normal quantities .......... 146
5.3 Multi-parameter inference.................................. 147
5.3.1 Di Multivariate Wald test........................... 147
5.3.2 D2 Combining test statistics*....................... 149
5.3.3 Ds Likelihood ratio test*........................... 150
5.3.4 Hi, D2 or H3?....................................... 152
5.4 Stepwise model selection .................................. 153
5.4.1 Variable selection techniques....................... 153
5.4.2 Computation......................................... 154
5.4.3 Model optimism...................................... 155
5.5 Parallel computation ...................................... 157
5.6 Conclusion ................................................ 158
5.7 Exercises ................................................. 158
Contents
xi
II Advanced techniques 161
6 Imputation in practice 163
6.1 Overview of modeling choices.................................. 163
6.2 Ignorable or nonignorable? ................................... 165
6.3 Model form and predictors ................................... 166
6.3.1 Model form.............................................. 166
6.3.2 Predictors.............................................. 167
6.4 Derived variables............................................. 170
6.4.1 Ratio of two variables.................................. 170
6.4.2 Interaction terms....................................... 175
6.4.3 Quadratic relations*.................................... 176
6.4.4 Compositional data*..................................... 177
6.4.5 Sum scores.............................................. 181
6.4.6 Conditional imputation.................................. 182
6.5 Algorithmic options .......................................... 184
6.5.1 Visit sequence ......................................... 184
6.5.2 Convergence ............................................ 187
6.6 Diagnostics................................................... 189
6.6.1 Model fit versus distributional discrepancy ............ 190
6.6.2 Diagnostic graphs ...................................... 190
6.7 Conclusion ................................................... 194
6.8 Exercises .................................................... 195
7 Multilevel multiple imputation 197
7.1 Introduction ................................................. 197
7.2 Notation for multilevel models ............................... 197
7.3 Missing values in multilevel data............................. 200
7.3.1 Practical issues in multilevel imputation............... 201
7.3.2 Ad-hoc solutions for multilevel data.................... 202
7.3.3 Likelihood solutions.................................... 203
7.4 Multilevel imputation by joint modeling....................... 204
7.5 Multilevel imputation by fully conditional specification . . . 205
7.5.1 Add cluster means of predictors......................... 206
7.5.2 Model cluster heterogeneity............................. 207
7.6 Continuous outcome............................................ 207
7.6.1 General principle....................................... 208
7.6.2 Methods................................................. 209
7.6.3 Example................................................. 209
7.7 Discrete outcome ............................................. 214
7.7.1 Methods................................................. 214
7.7.2 Example................................................. 215
7.8 Imputation of level-2 variable................................ 218
7.9 Comparative work.............................................. 219
7.10 Guidelines and advice......................................... 220
Contents
xii
7.10.1 Intercept-only model, missing outcomes.................
7.10.2 Random intercepts, missing level-1 predictor ..........
7.10.3 Random intercepts, contextual model ...................
7.10.4 Random intercepts, missing level-2 predictor ..........
7.10.5 Random intercepts, interactions........................
7.10.6 Random slopes, missing outcomes and predictors . . .
7.10.7 Random slopes, interactions............................
7.10.8 Recipes................................................
7.11 Future research ...............................................
222
222
224
226
228
232
234
238
240
8 Individual causal effects 241
8.1 Need for individual causal effects.......................... 241
8.2 Problem of causal inference................................. 243
8.3 Framework .................................................. 245
8.4 Generating imputations by FCS .............................. 246
8.4.1 Naive FCS ........................................... 246
8.4.2 FCS with a prior for p............................. 247
8.4.3 Extensions .......................................... 253
8.5 Bibliographic notes ........................................ 254
III Case studies 257
9 Measurement issues 259
9.1 Too many columns............................................ 259
9.1.1 Scientific question ................................. 260
9.1.2 Leiden 85+ Cohort.................................... 260
9.1.3 Data exploration..................................... 261
9.1.4 Outflux.............................................. 263
9.1.5 Finding problems: loggedEvents....................... 265
9.1.6 Quick predictor selection: quickpred................. 267
9.1.7 Generating the imputations........................... 268
9.1.8 A further improvement: Survival as predictor variable 270
9.1.9 Some guidance........................................ 270
9.2 Sensitivity analysis ....................................... 271
9.2.1 Causes and consequences of missing data.............. 272
9.2.2 Scenarios............................................ 274
9.2.3 Generating imputations under the ^-adjustment . . . 274
9.2.4 Complete-data model ................................. 275
9.2.5 Conclusion........................................... 277
9.3 Correct prevalence estimates from self-reported data........ 277
9.3.1 Description of the problem .......................... 277
9.3.2 Don’t count on predictions .......................... 278
9.3.3 The main idea........................................ 280
9.3.4 Data................................................. 281
9.3.5 Application.......................................... 281
Contents
xiii
9.3.6 Conclusion.......................................... 283
9.4 Enhancing comparability .................................... 283
9.4.1 Description of the problem.......................... 283
9.4.2 Full dependence: Simple equating.................... 284
9.4.3 Independence: Imputation without a bridge study . . 286
9.4.4 Fully dependent or independent?..................... 288
9.4.5 Imputation using a bridge study..................... 289
9.4.6 Interpretation...................................... 292
9.4.7 Conclusion.......................................... 293
9.5 Exercises .................................................. 294
10 Selection issues 295
10.1 Correcting for selective drop-out........................... 295
10.1.1 POPS study: 19 years follow-up...................... 295
10.1.2 Characterization of the drop-out.................... 296
10.1.3 Imputation model.................................... 296
10.1.4 A solution “that does not look good” ............... 299
10.1.5 Results ............................................ 301
10.1.6 Conclusion.......................................... 302
10.2 Correcting for nonresponse ................................. 302
10.2.1 Fifth Dutch Growth Study............................ 303
10.2.2 Nonresponse ........................................ 303
10.2.3 Comparison to known population totals............... 304
10.2.4 Augmenting the sample............................... 304
10.2.5 Imputation model.................................... 306
10.2.6 Influence of nonresponse on final height............ 307
10.2.7 Discussion.......................................... 308
10.3 Exercises ............................................... 309 11
11 Longitudinal data 311
11.1 Long and wide format........................................ 311
11.2 SE Fireworks Disaster Study................................. 313
11.2.1 Intention to treat................................... 314
11.2.2 Imputation model..................................... 315
11.2.3 Inspecting imputations............................... 317
11.2.4 Complete-data model.................................. 318
11.2.5 Results from the complete-data model................. 319
11.3 Time raster imputation ..................................... 320
11.3.1 Change score......................................... 321
11.3.2 Scientific question: Critical periods................ 322
11.3.3 Broken stick model*.................................. 324
11.3.4 Terneuzen Birth Cohort............................... 326
11.3.5 Shrinkage and the change score*...................... 328
11.3.6 Imputation........................................... 328
11.3.7 Complete-data model ................................. 330
xiv Contents
11.4 Conclusion .............................................. 332
11.5 Exercises ............................................... 334
IV Extensions 337
12 Conclusion 339
12.1 Some dangers, some do’s and some don’ts.................... 339
12.1.1 Some dangers...................................... 339
12.1.2 Some do’s......................................... 340
12.1.3 Some don’ts....................................... 341
12.2 Reporting.................................................. 342
12.2.1 Reporting guidelines................................ 343
12.2.2 Template............................................ 344
12.3 Other applications........................................ 345
12.3.1 Synthetic datasets for data protection.............. 345
12.3.2 Analysis of coarsened data.......................... 345
12.3.3 File matching of multiple datasets.................. 346
12.3.4 Planned missing data for efficient designs.......... 346
12.3.5 Adjusting for verification bias..................... 347
12.4 Future developments ....................................... 347
12.4.1 Derived variables................................... 347
12.4.2 Algorithms for blocks and batches................... 347
12.4.3 Nested imputation................................... 348
12.4.4 Better trials with dynamic treatment regimes........ 348
12.4.5 Distribution-free pooling rules..................... 348
12.4.6 Improved diagnostic techniques...................... 349
12.4.7 Building block in modular statistics................ 349
12.5 Exercises ................................................. 349
References 351
Author index 393
Subject index
405
|
| any_adam_object | 1 |
| author | Buuren, Stef van |
| author_GND | (DE-588)1070113697 |
| author_facet | Buuren, Stef van |
| author_role | aut |
| author_sort | Buuren, Stef van |
| author_variant | s v b sv svb |
| building | Verbundindex |
| bvnumber | BV045138307 |
| classification_rvk | QH 235 SK 830 |
| ctrlnum | (OCoLC)1037843784 (DE-599)GBV1019585366 |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | Second edition |
| format | Book |
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| id | DE-604.BV045138307 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:09:46Z |
| institution | BVB |
| isbn | 9781138588318 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030528167 |
| oclc_num | 1037843784 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-703 DE-20 DE-N2 DE-11 DE-739 DE-188 |
| owner_facet | DE-473 DE-BY-UBG DE-703 DE-20 DE-N2 DE-11 DE-739 DE-188 |
| physical | xxvii, 415 Seiten Diagramme (teilweise farbig) |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | CRC Press |
| record_format | marc |
| series2 | Chapman & Hall/CRC interdisciplinary statistics series A Chapman & Hall book |
| spelling | Buuren, Stef van Verfasser (DE-588)1070113697 aut Flexible imputation of missing data Stef van Buuren Second edition Boca Raton, Florida CRC Press 2018 © 2018 xxvii, 415 Seiten Diagramme (teilweise farbig) txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC interdisciplinary statistics series A Chapman & Hall book Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf Imputationstechnik (DE-588)4609617-6 gnd rswk-swf Zurechnung (DE-588)4068129-4 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Fehlende Daten (DE-588)4264715-0 gnd rswk-swf Multivariate analysis Multiple imputation (Statistics) Missing observations (Statistics) Multivariate Analyse (DE-588)4040708-1 s R Programm (DE-588)4705956-4 s Fehlende Daten (DE-588)4264715-0 s Zurechnung (DE-588)4068129-4 s DE-604 Imputationstechnik (DE-588)4609617-6 s Erscheint auch als Online-Ausgabe, ebook 978-0-429-49225-9 Erscheint auch als Online-Ausgabe, web pdf 978-0-429-96035-2 Erscheint auch als Online-Ausgabe, epub 978-0-429-96034-5 Erscheint auch als Online-Ausgabe, mobi / kindle 978-0-429-96033-8 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030528167&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Buuren, Stef van Flexible imputation of missing data Multivariate Analyse (DE-588)4040708-1 gnd Imputationstechnik (DE-588)4609617-6 gnd Zurechnung (DE-588)4068129-4 gnd R Programm (DE-588)4705956-4 gnd Fehlende Daten (DE-588)4264715-0 gnd |
| subject_GND | (DE-588)4040708-1 (DE-588)4609617-6 (DE-588)4068129-4 (DE-588)4705956-4 (DE-588)4264715-0 |
| title | Flexible imputation of missing data |
| title_auth | Flexible imputation of missing data |
| title_exact_search | Flexible imputation of missing data |
| title_full | Flexible imputation of missing data Stef van Buuren |
| title_fullStr | Flexible imputation of missing data Stef van Buuren |
| title_full_unstemmed | Flexible imputation of missing data Stef van Buuren |
| title_short | Flexible imputation of missing data |
| title_sort | flexible imputation of missing data |
| topic | Multivariate Analyse (DE-588)4040708-1 gnd Imputationstechnik (DE-588)4609617-6 gnd Zurechnung (DE-588)4068129-4 gnd R Programm (DE-588)4705956-4 gnd Fehlende Daten (DE-588)4264715-0 gnd |
| topic_facet | Multivariate Analyse Imputationstechnik Zurechnung R Programm Fehlende Daten |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030528167&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT buurenstefvan flexibleimputationofmissingdata |