Variable selection with multiply-imputed datasets: choosing between stacked and grouped methods Article Swipe
Jiacong Du
,
Jonathan Boss
,
Peisong Han
,
Lauren J. Beesley
,
Stephen A. Goutman
,
Stuart Batterman
,
Eva L. Feldman
,
Bhramar Mukherjee
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2003.07398
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2003.07398
Penalized regression methods, such as lasso and elastic net, are used in many biomedical applications when simultaneous regression coefficient estimation and variable selection is desired. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors, making it difficult to ascertain a final active set without resorting to ad hoc combination rules. In this paper we consider a general class of penalized objective functions which, by construction, force selection of the same variables across multiply-imputed datasets. By pooling objective functions across imputations, optimization is then performed jointly over all imputed datasets rather than separately for each dataset. We consider two objective function formulations that exist in the literature, which we will refer to as "stacked" and "grouped" objective functions. Building on existing work, we (a) derive and implement efficient cyclic coordinate descent and majorization-minimization optimization algorithms for both continuous and binary outcome data, (b) incorporate adaptive shrinkage penalties, (c) compare these methods through simulation, and (d) develop an R package miselect for easy implementation. Simulations demonstrate that the "stacked" objective function approaches tend to be more computationally efficient and have better estimation and selection properties. We apply these methods to data from the University of Michigan ALS Patients Repository (UMAPR) which aims to identify the association between persistent organic pollutants and ALS risk.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2003.07398
- https://arxiv.org/pdf/2003.07398
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4287826080
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- OpenAlex ID
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https://openalex.org/W4287826080Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2003.07398Digital Object Identifier
- Title
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Variable selection with multiply-imputed datasets: choosing between stacked and grouped methodsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-03-16Full publication date if available
- Authors
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Jiacong Du, Jonathan Boss, Peisong Han, Lauren J. Beesley, Stephen A. Goutman, Stuart Batterman, Eva L. Feldman, Bhramar MukherjeeList of authors in order
- Landing page
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https://arxiv.org/abs/2003.07398Publisher landing page
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https://arxiv.org/pdf/2003.07398Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2003.07398Direct OA link when available
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Missing data, Lasso (programming language), Imputation (statistics), Computer science, Feature selection, Coordinate descent, Pooling, Data mining, Regression, Elastic net regularization, Mathematical optimization, Algorithm, Machine learning, Artificial intelligence, Mathematics, Statistics, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.that | 127, 190 |
| abstract_inverted_index.then | 108 |
| abstract_inverted_index.this | 77 |
| abstract_inverted_index.used | 10 |
| abstract_inverted_index.when | 15, 35 |
| abstract_inverted_index.will | 51, 134 |
| abstract_inverted_index.apply | 210 |
| abstract_inverted_index.class | 83 |
| abstract_inverted_index.data, | 166 |
| abstract_inverted_index.exist | 128 |
| abstract_inverted_index.final | 66 |
| abstract_inverted_index.force | 91 |
| abstract_inverted_index.lasso | 5 |
| abstract_inverted_index.paper | 78 |
| abstract_inverted_index.refer | 135 |
| abstract_inverted_index.risk. | 236 |
| abstract_inverted_index.these | 32, 174, 211 |
| abstract_inverted_index.using | 39 |
| abstract_inverted_index.which | 132, 224 |
| abstract_inverted_index.work, | 146 |
| abstract_inverted_index.across | 97, 104 |
| abstract_inverted_index.active | 67 |
| abstract_inverted_index.better | 204 |
| abstract_inverted_index.binary | 164 |
| abstract_inverted_index.cyclic | 153 |
| abstract_inverted_index.derive | 149 |
| abstract_inverted_index.likely | 52 |
| abstract_inverted_index.making | 60 |
| abstract_inverted_index.rather | 115 |
| abstract_inverted_index.rules. | 75 |
| abstract_inverted_index.which, | 88 |
| abstract_inverted_index.(UMAPR) | 223 |
| abstract_inverted_index.between | 230 |
| abstract_inverted_index.compare | 173 |
| abstract_inverted_index.dataset | 50 |
| abstract_inverted_index.descent | 155 |
| abstract_inverted_index.develop | 180 |
| abstract_inverted_index.elastic | 7 |
| abstract_inverted_index.general | 82 |
| abstract_inverted_index.handled | 38 |
| abstract_inverted_index.imputed | 49, 113 |
| abstract_inverted_index.jointly | 110 |
| abstract_inverted_index.methods | 175, 212 |
| abstract_inverted_index.missing | 26 |
| abstract_inverted_index.organic | 232 |
| abstract_inverted_index.outcome | 165 |
| abstract_inverted_index.package | 183 |
| abstract_inverted_index.pooling | 101 |
| abstract_inverted_index.through | 176 |
| abstract_inverted_index.without | 69 |
| abstract_inverted_index.Applying | 42 |
| abstract_inverted_index.Building | 143 |
| abstract_inverted_index.However, | 25 |
| abstract_inverted_index.Michigan | 219 |
| abstract_inverted_index.Patients | 221 |
| abstract_inverted_index.adaptive | 169 |
| abstract_inverted_index.consider | 80, 122 |
| abstract_inverted_index.dataset. | 120 |
| abstract_inverted_index.datasets | 114 |
| abstract_inverted_index.desired. | 24 |
| abstract_inverted_index.existing | 145 |
| abstract_inverted_index.function | 125, 194 |
| abstract_inverted_index.identify | 227 |
| abstract_inverted_index.methods, | 2, 33 |
| abstract_inverted_index.miselect | 184 |
| abstract_inverted_index.multiple | 40 |
| abstract_inverted_index.selected | 58 |
| abstract_inverted_index.variable | 21, 44 |
| abstract_inverted_index."grouped" | 140 |
| abstract_inverted_index."stacked" | 138, 192 |
| abstract_inverted_index.Penalized | 0 |
| abstract_inverted_index.algorithm | 46 |
| abstract_inverted_index.ascertain | 64 |
| abstract_inverted_index.datasets. | 99 |
| abstract_inverted_index.different | 55 |
| abstract_inverted_index.difficult | 62 |
| abstract_inverted_index.efficient | 152, 201 |
| abstract_inverted_index.functions | 87, 103 |
| abstract_inverted_index.implement | 151 |
| abstract_inverted_index.objective | 86, 102, 124, 141, 193 |
| abstract_inverted_index.penalized | 85 |
| abstract_inverted_index.performed | 109 |
| abstract_inverted_index.resorting | 70 |
| abstract_inverted_index.selection | 22, 45, 92, 207 |
| abstract_inverted_index.shrinkage | 170 |
| abstract_inverted_index.variables | 96 |
| abstract_inverted_index.Repository | 222 |
| abstract_inverted_index.University | 217 |
| abstract_inverted_index.algorithms | 159 |
| abstract_inverted_index.approaches | 195 |
| abstract_inverted_index.biomedical | 13 |
| abstract_inverted_index.continuous | 162 |
| abstract_inverted_index.coordinate | 154 |
| abstract_inverted_index.estimation | 19, 205 |
| abstract_inverted_index.functions. | 142 |
| abstract_inverted_index.penalties, | 171 |
| abstract_inverted_index.persistent | 231 |
| abstract_inverted_index.pollutants | 233 |
| abstract_inverted_index.regression | 1, 17 |
| abstract_inverted_index.separately | 117 |
| abstract_inverted_index.Simulations | 188 |
| abstract_inverted_index.association | 229 |
| abstract_inverted_index.coefficient | 18 |
| abstract_inverted_index.combination | 74 |
| abstract_inverted_index.complicates | 28 |
| abstract_inverted_index.demonstrate | 189 |
| abstract_inverted_index.imputation. | 41 |
| abstract_inverted_index.incorporate | 168 |
| abstract_inverted_index.literature, | 131 |
| abstract_inverted_index.missingness | 36 |
| abstract_inverted_index.predictors, | 59 |
| abstract_inverted_index.properties. | 208 |
| abstract_inverted_index.simulation, | 177 |
| abstract_inverted_index.applications | 14 |
| abstract_inverted_index.formulations | 126 |
| abstract_inverted_index.imputations, | 105 |
| abstract_inverted_index.optimization | 106, 158 |
| abstract_inverted_index.particularly | 34 |
| abstract_inverted_index.simultaneous | 16 |
| abstract_inverted_index.construction, | 90 |
| abstract_inverted_index.implementation | 30 |
| abstract_inverted_index.computationally | 200 |
| abstract_inverted_index.implementation. | 187 |
| abstract_inverted_index.multiply-imputed | 98 |
| abstract_inverted_index.majorization-minimization | 157 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |