Evidence map›Paper›PMID 36644406›Full record

ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2022

Variable selection with multiply-imputed datasets: choosing between stacked and grouped methods.

Jiacong Du, Jonathan Boss, Peisong Han, Lauren J Beesley, Michael Kleinsasser, Stephen A Goutman, Stuart Batterman, Eva L Feldman, Bhramar Mukherjee

Abstract read
In one paragraph

Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jiacong DuDepartment of Biostatistics, University of Michigan, Ann Arbor, MI.
Jonathan BossDepartment of Biostatistics, University of Michigan, Ann Arbor, MI.
Peisong HanDepartment of Biostatistics, University of Michigan, Ann Arbor, MI.
Lauren J BeesleyDepartment of Biostatistics, University of Michigan, Ann Arbor, MI.
Michael KleinsasserDepartment of Biostatistics, University of Michigan, Ann Arbor, MI.
Stephen A GoutmanDepartment of Neurology, University of Michigan, Ann Arbor, MI.
Stuart BattermanDepartment of Environmental Health Science, University of Michigan, Ann Arbor, MI.
Eva L FeldmanDepartment of Neurology, University of Michigan, Ann Arbor, MI.
Bhramar MukherjeeDepartment of Biostatistics, University of Michigan, Ann Arbor, MI.

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CELESTE Leigh PEARCE · 1988 to 2026
$178.2M
Strategic Vision & Impact on Environmental HealthP30ES017885 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI John D. Meeker · 2011 to 2026
$21.5M
Mapping the ALS Exposome to Gain New Insights into Disease Risk and PathogenesisR01ES030049 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BATTERMAN, STUART A, FELDMAN, EVA LUCILLE · 2020 to 2024
$3.5M
Developing novel strategies for personalized treatment and prevention of ALS: Leveraging the global exposome, genome, epigenome, metabolome, and inflammasome with data science in a case/control cohortR01NS127188 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BATTERMAN, STUART A, FELDMAN, EVA LUCILLE · 2021 to 2024
$3.4M
Impact of Geospatial Factors and Environmental Pollutants on Amyotrophic Lateral Sclerosis in the State of MichiganK23ES027221 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GOUTMAN, STEPHEN · 2017 to 2021
$1.0M
NCI NIH HHS P30 CA046592NIEHS NIH HHS K23 ES027221NIEHS NIH HHS P30 ES017885NIEHS NIH HHS R01 ES030049NINDS NIH HHS R01 NS127188
6 · The paper itself

Abstract

Penalized regression methods are used in many biomedical applications for variable selection and simultaneous coefficient estimation. 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. This paper considers a general class of penalized objective functions which, by construction, force selection of the same variables across 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 continuous and binary outcome data, (b) incorporate adaptive shrinkage penalties, (c) compare these methods through simulation, and (d) develop an R package

Indexed as

Elastic NetGroup LASSOMajorization-MinimizationMissing DataMultiple ImputationPooled Objective Function

Identifiers

PMID36644406
PMCPMC9838615

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.