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Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis
Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We emp...
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| Publicat a: | J Appl Stat |
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| Autors principals: | , , , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
2016
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5047523/ https://ncbi.nlm.nih.gov/pubmed/27713593 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1080/02664763.2016.1155110 |
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