Improved conditional imputation for linear regression with a randomly censored predictor Article Swipe
Folefac Atem
,
Emmanuel Sampene
,
Thomas Greene
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1177/0962280217727033
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1177/0962280217727033
This article describes a nonparametric conditional imputation analytic method for randomly censored covariates in linear regression. While some existing methods make assumptions about the distribution of covariates or underestimate standard error due to lack of imputation error, the proposed approach is distribution-free and utilizes resampling to correct for variance underestimation. The performance of the novel method is assessed using simulations, and results are contrasted with methods currently used for a limit of detection censored design, including the complete case approach and other nonparametric approaches. Theoretical justifications for the proposed method are provided, and its application is demonstrated through a study of association between lipoprotein cholesterol in offspring and parental history of cardiovascular disease.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/0962280217727033
- OA Status
- green
- Cited By
- 15
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2748809547
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2748809547Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/0962280217727033Digital Object Identifier
- Title
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Improved conditional imputation for linear regression with a randomly censored predictorWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
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2017-08-22Full publication date if available
- Authors
-
Folefac Atem, Emmanuel Sampene, Thomas GreeneList of authors in order
- Landing page
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https://doi.org/10.1177/0962280217727033Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/5826819Direct OA link when available
- Concepts
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Imputation (statistics), Covariate, Resampling, Statistics, Nonparametric statistics, Regression, Econometrics, Linear regression, Mathematics, Computer science, Missing dataTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2023: 4, 2022: 4, 2020: 3, 2019: 2Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2017-08-22 |
| publication_year | 2017 |
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